客户信用风险预测模型.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 客户信用风险预测"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 一、 任务描述与分析\n",
    "### 一、数据集描述\n",
    "\n",
    "我们选择的数据集包含了1000个样本，每个样本有20个属性和1个类别标签。属性包括了贷款人的个人信息、贷款的金额、期限、目的、信用历史等，类别标签表示贷款人的信用风险等级。  \n",
    "下表为客户信用风险数据包含的各类属性及其含义\n",
    "\n",
    "|属性名称|\t含义|\n",
    "|   :----- |  :----- |\n",
    "|hecking_status|\t支票账户的状态，无账户、余额低于200欧元，余额高于200欧元。|\n",
    "|duration|\t贷款的期限，以月为单位。|\n",
    "|credit_history|\t贷款人的信用历史，如无信用记录，所有贷款已还清，有贷款正在还，有逾期记录，或有严重违约记录。|\n",
    "|purpose|\t贷款的目的，如购买汽车，家具，电器，教育，或其他。|\n",
    "|credit_amount|\t贷款的金额，以欧元为单位。|\n",
    "|savings_status|\t储蓄账户的状态，如无账户，余额低于100欧元，余额在100-500欧元之间，余额在500-1000欧元之间，或余额高于1000欧元。|\n",
    "|employmen|\t就业状况，如失业或低于一年，一年到四年，四年到七年，或超过七年。|\n",
    "|installment_commitment|\t每月还款占可支配收入的百分比。|\n",
    "|personal_statu|\t个人状态，如性别和婚姻状况。|\n",
    "|other_parties|\t是否有其他担保人或共同借款人。|\n",
    "|residence_since|\t在当前住址居住的时间，以年为单位。|\n",
    "|property_magnitude\t|拥有的财产类型，如无财产，汽车，房屋，或其他。|\n",
    "|age|\t贷款人的年龄，以年为单位。|\n",
    "|other_payment_plans\t|是否有其他还款计划或债务。|\n",
    "|housing\t|住房状况，如租房，自有房屋，或免费住房。|\n",
    "|existing_credits\t|已有的信用卡或贷款数量。|\n",
    "|job\t|职业类型，如无技能或半技能工人，技能工人或员工，管理者或自雇人士等。|\n",
    "|num_dependents\t|负担的家属数量。|\n",
    "|own_telephone\t|是否拥有固定电话。|\n",
    "|foreign_worke\t|是否是外国工作者。|\n",
    "|class\t|信用风险等级，如好（无违约风险）或坏（有违约风险）。|\n",
    "\n",
    "\n",
    "### 二、任务\n",
    "\n",
    "将这个数据集用于训练和测试信用风险模型，预测贷款人的信用风险。这是一个二分类任务，最终模型输出的结果是将贷款人的信用分类为good或bad。\n",
    "\n",
    "### 三、目标函数\n",
    "\n",
    "项目最终使用的模型是梯度提升树（Gradient Boosting Classifier），它是一种集成学习的方法，通过组合多个弱分类器来构建一个强分类器。梯度提升树的基本思想是在每一轮迭代中，根据损失函数的负梯度来拟合一个新的分类器，并将其加入到现有的分类器中，从而不断提升模型的预测能力。\n",
    "\n",
    "梯度提升树可以优化任意可微分的损失函数，模型选用的损失函数是对数损失。\n",
    "\n",
    "对数损失，也称为交叉熵损失，适用于二分类或多分类问题，对数损失的公式：\n",
    "$L(y,p)=-\\sum_{i=1}^N\\sum_{j=1}^Ky_{ij}logp_{ij}$\n",
    "\n",
    "其中，N是样本数量，K是类别数量，$y_{ij}$是一个二值指标，表示第i个样本是否属于第j个类别，$p_{ij}$是模型预测第i个样本属于第j个类别的概率。\n",
    "对数损失的含义为若模型预测的概率值和真实标签一致，那么对数损失趋近于0，如果模型预测的概率值和真实标签相反，那么对数损失趋近于无穷大。因此，对数损失可以反映模型的分类效果和置信度，越小越好。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、数据可视化及统计分析\n",
    "### 一、原数据集的可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图展示了原数据的信息。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checking_status</th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>savings_status</th>\n",
       "      <th>employment</th>\n",
       "      <th>installment_commitment</th>\n",
       "      <th>personal_status</th>\n",
       "      <th>other_parties</th>\n",
       "      <th>...</th>\n",
       "      <th>property_magnitude</th>\n",
       "      <th>age</th>\n",
       "      <th>other_payment_plans</th>\n",
       "      <th>housing</th>\n",
       "      <th>existing_credits</th>\n",
       "      <th>job</th>\n",
       "      <th>num_dependents</th>\n",
       "      <th>own_telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "      <th>class</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>&lt;0</td>\n",
       "      <td>6</td>\n",
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       "      <td>radio/tv</td>\n",
       "      <td>1169</td>\n",
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       "      <td>&gt;=7</td>\n",
       "      <td>4</td>\n",
       "      <td>male single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
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       "      <th>1</th>\n",
       "      <td>0&lt;=X&lt;200</td>\n",
       "      <td>48</td>\n",
       "      <td>existing paid</td>\n",
       "      <td>radio/tv</td>\n",
       "      <td>5951</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>1&lt;=X&lt;4</td>\n",
       "      <td>2</td>\n",
       "      <td>female div/dep/mar</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
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       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
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       "      <th>2</th>\n",
       "      <td>no checking</td>\n",
       "      <td>12</td>\n",
       "      <td>critical/other existing credit</td>\n",
       "      <td>education</td>\n",
       "      <td>2096</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>4&lt;=X&lt;7</td>\n",
       "      <td>2</td>\n",
       "      <td>male single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>49</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled resident</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;0</td>\n",
       "      <td>42</td>\n",
       "      <td>existing paid</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>7882</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>4&lt;=X&lt;7</td>\n",
       "      <td>2</td>\n",
       "      <td>male single</td>\n",
       "      <td>guarantor</td>\n",
       "      <td>...</td>\n",
       "      <td>life insurance</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
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       "      <td>good</td>\n",
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       "      <th>4</th>\n",
       "      <td>&lt;0</td>\n",
       "      <td>24</td>\n",
       "      <td>delayed previously</td>\n",
       "      <td>new car</td>\n",
       "      <td>4870</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>1&lt;=X&lt;4</td>\n",
       "      <td>3</td>\n",
       "      <td>male single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>no known property</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled</td>\n",
       "      <td>2</td>\n",
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       "      <td>yes</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>no checking</td>\n",
       "      <td>12</td>\n",
       "      <td>existing paid</td>\n",
       "      <td>furniture/equipment</td>\n",
       "      <td>1736</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>4&lt;=X&lt;7</td>\n",
       "      <td>3</td>\n",
       "      <td>female div/dep/mar</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>real estate</td>\n",
       "      <td>31</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled resident</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>&lt;0</td>\n",
       "      <td>30</td>\n",
       "      <td>existing paid</td>\n",
       "      <td>used car</td>\n",
       "      <td>3857</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>1&lt;=X&lt;4</td>\n",
       "      <td>4</td>\n",
       "      <td>male div/sep</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>life insurance</td>\n",
       "      <td>40</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>high qualif/self emp/mgmt</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>no checking</td>\n",
       "      <td>12</td>\n",
       "      <td>existing paid</td>\n",
       "      <td>radio/tv</td>\n",
       "      <td>804</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>&gt;=7</td>\n",
       "      <td>4</td>\n",
       "      <td>male single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car</td>\n",
       "      <td>38</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>&lt;0</td>\n",
       "      <td>45</td>\n",
       "      <td>existing paid</td>\n",
       "      <td>radio/tv</td>\n",
       "      <td>1845</td>\n",
       "      <td>&lt;100</td>\n",
       "      <td>1&lt;=X&lt;4</td>\n",
       "      <td>4</td>\n",
       "      <td>male single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>no known property</td>\n",
       "      <td>23</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>bad</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0&lt;=X&lt;200</td>\n",
       "      <td>45</td>\n",
       "      <td>critical/other existing credit</td>\n",
       "      <td>used car</td>\n",
       "      <td>4576</td>\n",
       "      <td>100&lt;=X&lt;500</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>3</td>\n",
       "      <td>male single</td>\n",
       "      <td>none</td>\n",
       "      <td>...</td>\n",
       "      <td>car</td>\n",
       "      <td>27</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "      <td>good</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    checking_status  duration                  credit_history  \\\n",
       "0                <0         6  critical/other existing credit   \n",
       "1          0<=X<200        48                   existing paid   \n",
       "2       no checking        12  critical/other existing credit   \n",
       "3                <0        42                   existing paid   \n",
       "4                <0        24              delayed previously   \n",
       "..              ...       ...                             ...   \n",
       "995     no checking        12                   existing paid   \n",
       "996              <0        30                   existing paid   \n",
       "997     no checking        12                   existing paid   \n",
       "998              <0        45                   existing paid   \n",
       "999        0<=X<200        45  critical/other existing credit   \n",
       "\n",
       "                 purpose  credit_amount    savings_status  employment  \\\n",
       "0               radio/tv           1169  no known savings         >=7   \n",
       "1               radio/tv           5951              <100      1<=X<4   \n",
       "2              education           2096              <100      4<=X<7   \n",
       "3    furniture/equipment           7882              <100      4<=X<7   \n",
       "4                new car           4870              <100      1<=X<4   \n",
       "..                   ...            ...               ...         ...   \n",
       "995  furniture/equipment           1736              <100      4<=X<7   \n",
       "996             used car           3857              <100      1<=X<4   \n",
       "997             radio/tv            804              <100         >=7   \n",
       "998             radio/tv           1845              <100      1<=X<4   \n",
       "999             used car           4576        100<=X<500  unemployed   \n",
       "\n",
       "     installment_commitment     personal_status other_parties  ...  \\\n",
       "0                         4         male single          none  ...   \n",
       "1                         2  female div/dep/mar          none  ...   \n",
       "2                         2         male single          none  ...   \n",
       "3                         2         male single     guarantor  ...   \n",
       "4                         3         male single          none  ...   \n",
       "..                      ...                 ...           ...  ...   \n",
       "995                       3  female div/dep/mar          none  ...   \n",
       "996                       4        male div/sep          none  ...   \n",
       "997                       4         male single          none  ...   \n",
       "998                       4         male single          none  ...   \n",
       "999                       3         male single          none  ...   \n",
       "\n",
       "     property_magnitude age  other_payment_plans   housing existing_credits  \\\n",
       "0           real estate  67                 none       own                2   \n",
       "1           real estate  22                 none       own                1   \n",
       "2           real estate  49                 none       own                1   \n",
       "3        life insurance  45                 none  for free                1   \n",
       "4     no known property  53                 none  for free                2   \n",
       "..                  ...  ..                  ...       ...              ...   \n",
       "995         real estate  31                 none       own                1   \n",
       "996      life insurance  40                 none       own                1   \n",
       "997                 car  38                 none       own                1   \n",
       "998   no known property  23                 none  for free                1   \n",
       "999                 car  27                 none       own                1   \n",
       "\n",
       "                           job num_dependents  own_telephone foreign_worker  \\\n",
       "0                      skilled              1            yes            yes   \n",
       "1                      skilled              1           none            yes   \n",
       "2           unskilled resident              2           none            yes   \n",
       "3                      skilled              2           none            yes   \n",
       "4                      skilled              2           none            yes   \n",
       "..                         ...            ...            ...            ...   \n",
       "995         unskilled resident              1           none            yes   \n",
       "996  high qualif/self emp/mgmt              1            yes            yes   \n",
       "997                    skilled              1           none            yes   \n",
       "998                    skilled              1            yes            yes   \n",
       "999                    skilled              1           none            yes   \n",
       "\n",
       "    class  \n",
       "0    good  \n",
       "1     bad  \n",
       "2    good  \n",
       "3    good  \n",
       "4     bad  \n",
       "..    ...  \n",
       "995  good  \n",
       "996  good  \n",
       "997  good  \n",
       "998   bad  \n",
       "999  good  \n",
       "\n",
       "[1000 rows x 21 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "credit=pd.read_csv(\"./credit_customers.csv\")\n",
    "credit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图展示了原数据的20个属性及标签名称，可以看到原始数据集不存在缺失值和重复值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 21 columns):\n",
      " #   Column                  Non-Null Count  Dtype \n",
      "---  ------                  --------------  ----- \n",
      " 0   checking_status         1000 non-null   object\n",
      " 1   duration                1000 non-null   int64 \n",
      " 2   credit_history          1000 non-null   object\n",
      " 3   purpose                 1000 non-null   object\n",
      " 4   credit_amount           1000 non-null   int64 \n",
      " 5   savings_status          1000 non-null   object\n",
      " 6   employment              1000 non-null   object\n",
      " 7   installment_commitment  1000 non-null   int64 \n",
      " 8   personal_status         1000 non-null   object\n",
      " 9   other_parties           1000 non-null   object\n",
      " 10  residence_since         1000 non-null   int64 \n",
      " 11  property_magnitude      1000 non-null   object\n",
      " 12  age                     1000 non-null   int64 \n",
      " 13  other_payment_plans     1000 non-null   object\n",
      " 14  housing                 1000 non-null   object\n",
      " 15  existing_credits        1000 non-null   int64 \n",
      " 16  job                     1000 non-null   object\n",
      " 17  num_dependents          1000 non-null   int64 \n",
      " 18  own_telephone           1000 non-null   object\n",
      " 19  foreign_worker          1000 non-null   object\n",
      " 20  class                   1000 non-null   object\n",
      "dtypes: int64(7), object(14)\n",
      "memory usage: 164.2+ KB\n",
      "重复的数据行数： 0\n"
     ]
    }
   ],
   "source": [
    "credit.info()\n",
    "print(\"重复的数据行数：\",credit.duplicated().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking_status           False\n",
      "duration                  False\n",
      "credit_history            False\n",
      "purpose                   False\n",
      "credit_amount             False\n",
      "savings_status            False\n",
      "employment                False\n",
      "installment_commitment    False\n",
      "personal_status           False\n",
      "other_parties             False\n",
      "residence_since           False\n",
      "property_magnitude        False\n",
      "age                       False\n",
      "other_payment_plans       False\n",
      "housing                   False\n",
      "existing_credits          False\n",
      "job                       False\n",
      "num_dependents            False\n",
      "own_telephone             False\n",
      "foreign_worker            False\n",
      "class                     False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "# 查看数据集中是否有缺失值\n",
    "print(credit.isnull().any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图为原始数据以属性duration 和 credit amount 作为坐标轴绘制的散点图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_c = credit['credit_amount']\n",
    "y_c = credit['duration']\n",
    "c = credit['class']\n",
    "color_dict = {'bad': 'red', 'good': 'green'}\n",
    "\n",
    "scatter = plt.scatter(x_c, y_c, color=c.apply(color_dict.get), label=c)\n",
    "\n",
    "plt.xlabel('Credit Amount')\n",
    "plt.ylabel('Duration')\n",
    "\n",
    "handle =[ plt.Line2D([0],[0],color='g',label='good'),plt.Line2D([0],[0],color='r',label='bad')]\n",
    "plt.legend(handles = handle)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二、原数据集的统计分析"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 一、 描述性统计图\n",
    "下图为原数据集的描述性统计表，可以观察到数值型数据的分布不一致需要对数值型数据进行标准化，并且需要对类别型数据进行编码处理。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>installment_commitment</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>age</th>\n",
       "      <th>existing_credits</th>\n",
       "      <th>num_dependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.903000</td>\n",
       "      <td>3271.258000</td>\n",
       "      <td>2.973000</td>\n",
       "      <td>2.845000</td>\n",
       "      <td>35.546000</td>\n",
       "      <td>1.407000</td>\n",
       "      <td>1.155000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.058814</td>\n",
       "      <td>2822.736876</td>\n",
       "      <td>1.118715</td>\n",
       "      <td>1.103718</td>\n",
       "      <td>11.375469</td>\n",
       "      <td>0.577654</td>\n",
       "      <td>0.362086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1365.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2319.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>3972.250000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>72.000000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          duration  credit_amount  installment_commitment  residence_since  \\\n",
       "count  1000.000000    1000.000000             1000.000000      1000.000000   \n",
       "mean     20.903000    3271.258000                2.973000         2.845000   \n",
       "std      12.058814    2822.736876                1.118715         1.103718   \n",
       "min       4.000000     250.000000                1.000000         1.000000   \n",
       "25%      12.000000    1365.500000                2.000000         2.000000   \n",
       "50%      18.000000    2319.500000                3.000000         3.000000   \n",
       "75%      24.000000    3972.250000                4.000000         4.000000   \n",
       "max      72.000000   18424.000000                4.000000         4.000000   \n",
       "\n",
       "               age  existing_credits  num_dependents  \n",
       "count  1000.000000       1000.000000     1000.000000  \n",
       "mean     35.546000          1.407000        1.155000  \n",
       "std      11.375469          0.577654        0.362086  \n",
       "min      19.000000          1.000000        1.000000  \n",
       "25%      27.000000          1.000000        1.000000  \n",
       "50%      33.000000          1.000000        1.000000  \n",
       "75%      42.000000          2.000000        1.000000  \n",
       "max      75.000000          4.000000        2.000000  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 三、特征工程\n",
    "\n",
    "特征工程是指将原始数据转化为更适合机器学习模型的特征的过程，它可以提高模型的性能和泛化能力。在进行分类任务模型训练的时候，为了改善数据质量、让数据适应算法的要求，需要对数据进行数据预处理，处理异常值、数据格式等问题，提高数据的标准性、唯一性、有效性、正确性和一致性。在下面的特征工程中，通过特征编码对数据进行转换，可以将定性特征转换为定量特征，满足机器学习算法对模型的输入要求。同时也可以生成一些新的特征，增加了数据中的有效信息，提高了模型的表达能力和预测能力。\n",
    "\n",
    "### 一、 数据转换\n",
    "\n",
    "观察原数据发现存在类别型数据，需要对类别型数据进行编码：首先观察各个类别型数据的具体类别，并统计类别的占比。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: checking_status\n",
      "no checking    0.394\n",
      "<0             0.274\n",
      "0<=X<200       0.269\n",
      ">=200          0.063\n",
      "Name: checking_status, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: credit_history\n",
      "existing paid                     0.530\n",
      "critical/other existing credit    0.293\n",
      "delayed previously                0.088\n",
      "all paid                          0.049\n",
      "no credits/all paid               0.040\n",
      "Name: credit_history, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: purpose\n",
      "radio/tv               0.280\n",
      "new car                0.234\n",
      "furniture/equipment    0.181\n",
      "used car               0.103\n",
      "business               0.097\n",
      "education              0.050\n",
      "repairs                0.022\n",
      "domestic appliance     0.012\n",
      "other                  0.012\n",
      "retraining             0.009\n",
      "Name: purpose, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: savings_status\n",
      "<100                0.603\n",
      "no known savings    0.183\n",
      "100<=X<500          0.103\n",
      "500<=X<1000         0.063\n",
      ">=1000              0.048\n",
      "Name: savings_status, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: employment\n",
      "1<=X<4        0.339\n",
      ">=7           0.253\n",
      "4<=X<7        0.174\n",
      "<1            0.172\n",
      "unemployed    0.062\n",
      "Name: employment, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: personal_status\n",
      "male single           0.548\n",
      "female div/dep/mar    0.310\n",
      "male mar/wid          0.092\n",
      "male div/sep          0.050\n",
      "Name: personal_status, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: other_parties\n",
      "none            0.907\n",
      "guarantor       0.052\n",
      "co applicant    0.041\n",
      "Name: other_parties, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: property_magnitude\n",
      "car                  0.332\n",
      "real estate          0.282\n",
      "life insurance       0.232\n",
      "no known property    0.154\n",
      "Name: property_magnitude, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: other_payment_plans\n",
      "none      0.814\n",
      "bank      0.139\n",
      "stores    0.047\n",
      "Name: other_payment_plans, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: housing\n",
      "own         0.713\n",
      "rent        0.179\n",
      "for free    0.108\n",
      "Name: housing, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: job\n",
      "skilled                      0.630\n",
      "unskilled resident           0.200\n",
      "high qualif/self emp/mgmt    0.148\n",
      "unemp/unskilled non res      0.022\n",
      "Name: job, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: own_telephone\n",
      "none    0.596\n",
      "yes     0.404\n",
      "Name: own_telephone, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: foreign_worker\n",
      "yes    0.963\n",
      "no     0.037\n",
      "Name: foreign_worker, dtype: float64\n",
      "----------------------------------------\n",
      "Variable: class\n",
      "good    0.7\n",
      "bad     0.3\n",
      "Name: class, dtype: float64\n",
      "----------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 统计每个类别变量的比例\n",
    "for var in credit.select_dtypes(include=['object']).columns:\n",
    "    # 使用value_counts函数计算每个类别的频数\n",
    "    frequency = credit.select_dtypes(include=['object'])[var].value_counts()\n",
    "    # 计算总的观测数量\n",
    "    total_count = frequency.sum()\n",
    "    # 计算每个类别的比例\n",
    "    proportions = frequency / total_count\n",
    "    # 打印结果\n",
    "    print(\"Variable:\", var)\n",
    "    print(proportions)\n",
    "    print(\"----------------------------------------\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据得到的结果（下上图），选择以下无序的类别变量进行独热编码。无序变量：purpose、personal_status、other_parties、property_magnitude。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.get_dummies(credit,columns=['purpose', 'personal_status',\"other_parties\",\"property_magnitude\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但是为了避免维数灾难，不能对所有变量进行独热编码，因此对以下有序且只有两种类别的分类变量进行自定义编码。\n",
    "自定义编码分类变量为： checking_status、credit_history、savings_status、employment、other_payment_plans、housing、job、own_telephone、foreign_worker、class。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['checking_status','credit_history', 'savings_status', 'employment', 'other_payment_plans','housing', \n",
    "        'job','own_telephone','foreign_worker','class']\n",
    "# 映射字典\n",
    "col_dicts = {}\n",
    "col_dicts = {\n",
    "  'checking_status': {\n",
    "    'no checking': 0,\n",
    "    '<0': 1,\n",
    "    '0<=X<200': 2,\n",
    "    '>=200': 3\n",
    "  },\n",
    "             \n",
    "  'credit_history': {\n",
    "      'existing paid': 0,\n",
    "      'critical/other existing credit': 1,\n",
    "      'delayed previously': 2,\n",
    "      'all paid': 3,\n",
    "      'no credits/all paid': 4\n",
    "  },\n",
    "\n",
    "'savings_status': {\n",
    "      'no known savings': 0,\n",
    "      '<100': 1,\n",
    "      '100<=X<500': 2,\n",
    "      '500<=X<1000': 3,\n",
    "      '>=1000': 4\n",
    "  },\n",
    "\n",
    " 'employment': {\n",
    "  'unemployed': 0,\n",
    "  '<1': 1,\n",
    "  '1<=X<4': 2,\n",
    "  '4<=X<7': 3,\n",
    "  '>=7': 4\n",
    "  },\n",
    "\n",
    "'other_payment_plans':{\n",
    "    'none':0,\n",
    "    'bank':1,\n",
    "    'stores':2\n",
    "},\n",
    "      \n",
    " 'housing': {'for free': 0, 'rent': 1, 'own': 2},\n",
    "\n",
    " 'job': {\n",
    "  'unemp/unskilled non res': 0,\n",
    "  'unskilled resident': 1,\n",
    "  'skilled': 2,\n",
    "  'high qualif/self emp/mgmt': 3\n",
    "  },\n",
    "\n",
    "  'own_telephone': {'none': 0, 'yes': 1},\n",
    "\n",
    "  'foreign_worker': {'no': 1, 'yes': 0},\n",
    "\n",
    "  'class': {'bad': 0, 'good': 1}\n",
    " }\n",
    "\n",
    "\n",
    "for col in cols:\n",
    "    data[col] = credit[col].map(lambda x: x.strip())\n",
    "    data[col] = credit[col].map(col_dicts[col])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图编码后数据的数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checking_status</th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>savings_status</th>\n",
       "      <th>employment</th>\n",
       "      <th>installment_commitment</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>age</th>\n",
       "      <th>other_payment_plans</th>\n",
       "      <th>...</th>\n",
       "      <th>personal_status_male div/sep</th>\n",
       "      <th>personal_status_male mar/wid</th>\n",
       "      <th>personal_status_male single</th>\n",
       "      <th>other_parties_co applicant</th>\n",
       "      <th>other_parties_guarantor</th>\n",
       "      <th>other_parties_none</th>\n",
       "      <th>property_magnitude_car</th>\n",
       "      <th>property_magnitude_life insurance</th>\n",
       "      <th>property_magnitude_no known property</th>\n",
       "      <th>property_magnitude_real estate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1169</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>5951</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2096</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>7882</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>4870</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1736</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>3857</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>804</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>1845</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>4576</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     checking_status  duration  credit_history  credit_amount  savings_status  \\\n",
       "0                  1         6               1           1169               0   \n",
       "1                  2        48               0           5951               1   \n",
       "2                  0        12               1           2096               1   \n",
       "3                  1        42               0           7882               1   \n",
       "4                  1        24               2           4870               1   \n",
       "..               ...       ...             ...            ...             ...   \n",
       "995                0        12               0           1736               1   \n",
       "996                1        30               0           3857               1   \n",
       "997                0        12               0            804               1   \n",
       "998                1        45               0           1845               1   \n",
       "999                2        45               1           4576               2   \n",
       "\n",
       "     employment  installment_commitment  residence_since  age  \\\n",
       "0             4                       4                4   67   \n",
       "1             2                       2                2   22   \n",
       "2             3                       2                3   49   \n",
       "3             3                       2                4   45   \n",
       "4             2                       3                4   53   \n",
       "..          ...                     ...              ...  ...   \n",
       "995           3                       3                4   31   \n",
       "996           2                       4                4   40   \n",
       "997           4                       4                4   38   \n",
       "998           2                       4                4   23   \n",
       "999           0                       3                4   27   \n",
       "\n",
       "     other_payment_plans  ...  personal_status_male div/sep  \\\n",
       "0                      0  ...                             0   \n",
       "1                      0  ...                             0   \n",
       "2                      0  ...                             0   \n",
       "3                      0  ...                             0   \n",
       "4                      0  ...                             0   \n",
       "..                   ...  ...                           ...   \n",
       "995                    0  ...                             0   \n",
       "996                    0  ...                             1   \n",
       "997                    0  ...                             0   \n",
       "998                    0  ...                             0   \n",
       "999                    0  ...                             0   \n",
       "\n",
       "     personal_status_male mar/wid  personal_status_male single  \\\n",
       "0                               0                            1   \n",
       "1                               0                            0   \n",
       "2                               0                            1   \n",
       "3                               0                            1   \n",
       "4                               0                            1   \n",
       "..                            ...                          ...   \n",
       "995                             0                            0   \n",
       "996                             0                            0   \n",
       "997                             0                            1   \n",
       "998                             0                            1   \n",
       "999                             0                            1   \n",
       "\n",
       "     other_parties_co applicant  other_parties_guarantor  other_parties_none  \\\n",
       "0                             0                        0                   1   \n",
       "1                             0                        0                   1   \n",
       "2                             0                        0                   1   \n",
       "3                             0                        1                   0   \n",
       "4                             0                        0                   1   \n",
       "..                          ...                      ...                 ...   \n",
       "995                           0                        0                   1   \n",
       "996                           0                        0                   1   \n",
       "997                           0                        0                   1   \n",
       "998                           0                        0                   1   \n",
       "999                           0                        0                   1   \n",
       "\n",
       "     property_magnitude_car  property_magnitude_life insurance  \\\n",
       "0                         0                                  0   \n",
       "1                         0                                  0   \n",
       "2                         0                                  0   \n",
       "3                         0                                  1   \n",
       "4                         0                                  0   \n",
       "..                      ...                                ...   \n",
       "995                       0                                  0   \n",
       "996                       0                                  1   \n",
       "997                       1                                  0   \n",
       "998                       0                                  0   \n",
       "999                       1                                  0   \n",
       "\n",
       "     property_magnitude_no known property  property_magnitude_real estate  \n",
       "0                                       0                               1  \n",
       "1                                       0                               1  \n",
       "2                                       0                               1  \n",
       "3                                       0                               0  \n",
       "4                                       1                               0  \n",
       "..                                    ...                             ...  \n",
       "995                                     0                               1  \n",
       "996                                     0                               0  \n",
       "997                                     0                               0  \n",
       "998                                     1                               0  \n",
       "999                                     0                               0  \n",
       "\n",
       "[1000 rows x 38 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 打印编码后的数据集\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设特征集和目标变量\n",
    "y = data[\"class\"]\n",
    "feature_vars = data.drop('class', axis=1).columns\n",
    "X = data[feature_vars]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图是编码后数据的描述性统计图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checking_status</th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>savings_status</th>\n",
       "      <th>employment</th>\n",
       "      <th>installment_commitment</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>age</th>\n",
       "      <th>other_payment_plans</th>\n",
       "      <th>...</th>\n",
       "      <th>personal_status_male div/sep</th>\n",
       "      <th>personal_status_male mar/wid</th>\n",
       "      <th>personal_status_male single</th>\n",
       "      <th>other_parties_co applicant</th>\n",
       "      <th>other_parties_guarantor</th>\n",
       "      <th>other_parties_none</th>\n",
       "      <th>property_magnitude_car</th>\n",
       "      <th>property_magnitude_life insurance</th>\n",
       "      <th>property_magnitude_no known property</th>\n",
       "      <th>property_magnitude_real estate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.001000</td>\n",
       "      <td>20.903000</td>\n",
       "      <td>0.776000</td>\n",
       "      <td>3271.258000</td>\n",
       "      <td>1.190000</td>\n",
       "      <td>2.384000</td>\n",
       "      <td>2.973000</td>\n",
       "      <td>2.845000</td>\n",
       "      <td>35.546000</td>\n",
       "      <td>0.233000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.092000</td>\n",
       "      <td>0.54800</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.052000</td>\n",
       "      <td>0.907000</td>\n",
       "      <td>0.332000</td>\n",
       "      <td>0.23200</td>\n",
       "      <td>0.154000</td>\n",
       "      <td>0.282000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.957034</td>\n",
       "      <td>12.058814</td>\n",
       "      <td>1.060636</td>\n",
       "      <td>2822.736876</td>\n",
       "      <td>0.966869</td>\n",
       "      <td>1.208306</td>\n",
       "      <td>1.118715</td>\n",
       "      <td>1.103718</td>\n",
       "      <td>11.375469</td>\n",
       "      <td>0.522479</td>\n",
       "      <td>...</td>\n",
       "      <td>0.218054</td>\n",
       "      <td>0.289171</td>\n",
       "      <td>0.49794</td>\n",
       "      <td>0.198389</td>\n",
       "      <td>0.222138</td>\n",
       "      <td>0.290578</td>\n",
       "      <td>0.471167</td>\n",
       "      <td>0.42232</td>\n",
       "      <td>0.361129</td>\n",
       "      <td>0.450198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1365.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2319.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3972.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>18424.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       checking_status     duration  credit_history  credit_amount  \\\n",
       "count      1000.000000  1000.000000     1000.000000    1000.000000   \n",
       "mean          1.001000    20.903000        0.776000    3271.258000   \n",
       "std           0.957034    12.058814        1.060636    2822.736876   \n",
       "min           0.000000     4.000000        0.000000     250.000000   \n",
       "25%           0.000000    12.000000        0.000000    1365.500000   \n",
       "50%           1.000000    18.000000        0.000000    2319.500000   \n",
       "75%           2.000000    24.000000        1.000000    3972.250000   \n",
       "max           3.000000    72.000000        4.000000   18424.000000   \n",
       "\n",
       "       savings_status   employment  installment_commitment  residence_since  \\\n",
       "count     1000.000000  1000.000000             1000.000000      1000.000000   \n",
       "mean         1.190000     2.384000                2.973000         2.845000   \n",
       "std          0.966869     1.208306                1.118715         1.103718   \n",
       "min          0.000000     0.000000                1.000000         1.000000   \n",
       "25%          1.000000     2.000000                2.000000         2.000000   \n",
       "50%          1.000000     2.000000                3.000000         3.000000   \n",
       "75%          1.000000     4.000000                4.000000         4.000000   \n",
       "max          4.000000     4.000000                4.000000         4.000000   \n",
       "\n",
       "               age  other_payment_plans  ...  personal_status_male div/sep  \\\n",
       "count  1000.000000          1000.000000  ...                   1000.000000   \n",
       "mean     35.546000             0.233000  ...                      0.050000   \n",
       "std      11.375469             0.522479  ...                      0.218054   \n",
       "min      19.000000             0.000000  ...                      0.000000   \n",
       "25%      27.000000             0.000000  ...                      0.000000   \n",
       "50%      33.000000             0.000000  ...                      0.000000   \n",
       "75%      42.000000             0.000000  ...                      0.000000   \n",
       "max      75.000000             2.000000  ...                      1.000000   \n",
       "\n",
       "       personal_status_male mar/wid  personal_status_male single  \\\n",
       "count                   1000.000000                   1000.00000   \n",
       "mean                       0.092000                      0.54800   \n",
       "std                        0.289171                      0.49794   \n",
       "min                        0.000000                      0.00000   \n",
       "25%                        0.000000                      0.00000   \n",
       "50%                        0.000000                      1.00000   \n",
       "75%                        0.000000                      1.00000   \n",
       "max                        1.000000                      1.00000   \n",
       "\n",
       "       other_parties_co applicant  other_parties_guarantor  \\\n",
       "count                 1000.000000              1000.000000   \n",
       "mean                     0.041000                 0.052000   \n",
       "std                      0.198389                 0.222138   \n",
       "min                      0.000000                 0.000000   \n",
       "25%                      0.000000                 0.000000   \n",
       "50%                      0.000000                 0.000000   \n",
       "75%                      0.000000                 0.000000   \n",
       "max                      1.000000                 1.000000   \n",
       "\n",
       "       other_parties_none  property_magnitude_car  \\\n",
       "count         1000.000000             1000.000000   \n",
       "mean             0.907000                0.332000   \n",
       "std              0.290578                0.471167   \n",
       "min              0.000000                0.000000   \n",
       "25%              1.000000                0.000000   \n",
       "50%              1.000000                0.000000   \n",
       "75%              1.000000                1.000000   \n",
       "max              1.000000                1.000000   \n",
       "\n",
       "       property_magnitude_life insurance  \\\n",
       "count                         1000.00000   \n",
       "mean                             0.23200   \n",
       "std                              0.42232   \n",
       "min                              0.00000   \n",
       "25%                              0.00000   \n",
       "50%                              0.00000   \n",
       "75%                              0.00000   \n",
       "max                              1.00000   \n",
       "\n",
       "       property_magnitude_no known property  property_magnitude_real estate  \n",
       "count                           1000.000000                     1000.000000  \n",
       "mean                               0.154000                        0.282000  \n",
       "std                                0.361129                        0.450198  \n",
       "min                                0.000000                        0.000000  \n",
       "25%                                0.000000                        0.000000  \n",
       "50%                                0.000000                        0.000000  \n",
       "75%                                0.000000                        1.000000  \n",
       "max                                1.000000                        1.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "# 输出描述统计表\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_vars = ['duration', 'credit_amount', 'installment_commitment','residence_since','age','existing_credits','num_dependents']  # 数值型变量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 直方图\n",
    "# 选择前几列变量，剔除独热编码的变量\n",
    "data[numeric_vars].hist(figsize=(10, 8))\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图为编码后数据的直方图，可以看到数据在各个属性上的分布，可以看到分布并不均匀，需要对原数据进行标准化，下图为标准化后的数据集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>checking_status</th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>savings_status</th>\n",
       "      <th>employment</th>\n",
       "      <th>installment_commitment</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>age</th>\n",
       "      <th>other_payment_plans</th>\n",
       "      <th>...</th>\n",
       "      <th>personal_status_male div/sep</th>\n",
       "      <th>personal_status_male mar/wid</th>\n",
       "      <th>personal_status_male single</th>\n",
       "      <th>other_parties_co applicant</th>\n",
       "      <th>other_parties_guarantor</th>\n",
       "      <th>other_parties_none</th>\n",
       "      <th>property_magnitude_car</th>\n",
       "      <th>property_magnitude_life insurance</th>\n",
       "      <th>property_magnitude_no known property</th>\n",
       "      <th>property_magnitude_real estate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>-1.236478</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.745131</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.918477</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>2.766456</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2.248194</td>\n",
       "      <td>0</td>\n",
       "      <td>0.949817</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.870183</td>\n",
       "      <td>-0.765977</td>\n",
       "      <td>-1.191404</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.738668</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.416562</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.870183</td>\n",
       "      <td>0.140505</td>\n",
       "      <td>1.183312</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1.750384</td>\n",
       "      <td>0</td>\n",
       "      <td>1.634247</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.870183</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>0.831502</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.256953</td>\n",
       "      <td>2</td>\n",
       "      <td>0.566664</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.024147</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>1.535122</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.738668</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.544162</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.024147</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>-0.399832</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1</td>\n",
       "      <td>0.754763</td>\n",
       "      <td>0</td>\n",
       "      <td>0.207612</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.918477</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>0.391740</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.738668</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.874503</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0.918477</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>0.215835</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1</td>\n",
       "      <td>1.999289</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.505528</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.918477</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>-1.103451</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>2</td>\n",
       "      <td>1.999289</td>\n",
       "      <td>1</td>\n",
       "      <td>0.462457</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.024147</td>\n",
       "      <td>1.046987</td>\n",
       "      <td>-0.751642</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     checking_status  duration  credit_history  credit_amount  savings_status  \\\n",
       "0                  1 -1.236478               1      -0.745131               0   \n",
       "1                  2  2.248194               0       0.949817               1   \n",
       "2                  0 -0.738668               1      -0.416562               1   \n",
       "3                  1  1.750384               0       1.634247               1   \n",
       "4                  1  0.256953               2       0.566664               1   \n",
       "..               ...       ...             ...            ...             ...   \n",
       "995                0 -0.738668               0      -0.544162               1   \n",
       "996                1  0.754763               0       0.207612               1   \n",
       "997                0 -0.738668               0      -0.874503               1   \n",
       "998                1  1.999289               0      -0.505528               1   \n",
       "999                2  1.999289               1       0.462457               2   \n",
       "\n",
       "     employment  installment_commitment  residence_since       age  \\\n",
       "0             4                0.918477         1.046987  2.766456   \n",
       "1             2               -0.870183        -0.765977 -1.191404   \n",
       "2             3               -0.870183         0.140505  1.183312   \n",
       "3             3               -0.870183         1.046987  0.831502   \n",
       "4             2                0.024147         1.046987  1.535122   \n",
       "..          ...                     ...              ...       ...   \n",
       "995           3                0.024147         1.046987 -0.399832   \n",
       "996           2                0.918477         1.046987  0.391740   \n",
       "997           4                0.918477         1.046987  0.215835   \n",
       "998           2                0.918477         1.046987 -1.103451   \n",
       "999           0                0.024147         1.046987 -0.751642   \n",
       "\n",
       "     other_payment_plans  ...  personal_status_male div/sep  \\\n",
       "0                      0  ...                             0   \n",
       "1                      0  ...                             0   \n",
       "2                      0  ...                             0   \n",
       "3                      0  ...                             0   \n",
       "4                      0  ...                             0   \n",
       "..                   ...  ...                           ...   \n",
       "995                    0  ...                             0   \n",
       "996                    0  ...                             1   \n",
       "997                    0  ...                             0   \n",
       "998                    0  ...                             0   \n",
       "999                    0  ...                             0   \n",
       "\n",
       "     personal_status_male mar/wid  personal_status_male single  \\\n",
       "0                               0                            1   \n",
       "1                               0                            0   \n",
       "2                               0                            1   \n",
       "3                               0                            1   \n",
       "4                               0                            1   \n",
       "..                            ...                          ...   \n",
       "995                             0                            0   \n",
       "996                             0                            0   \n",
       "997                             0                            1   \n",
       "998                             0                            1   \n",
       "999                             0                            1   \n",
       "\n",
       "     other_parties_co applicant  other_parties_guarantor  other_parties_none  \\\n",
       "0                             0                        0                   1   \n",
       "1                             0                        0                   1   \n",
       "2                             0                        0                   1   \n",
       "3                             0                        1                   0   \n",
       "4                             0                        0                   1   \n",
       "..                          ...                      ...                 ...   \n",
       "995                           0                        0                   1   \n",
       "996                           0                        0                   1   \n",
       "997                           0                        0                   1   \n",
       "998                           0                        0                   1   \n",
       "999                           0                        0                   1   \n",
       "\n",
       "     property_magnitude_car  property_magnitude_life insurance  \\\n",
       "0                         0                                  0   \n",
       "1                         0                                  0   \n",
       "2                         0                                  0   \n",
       "3                         0                                  1   \n",
       "4                         0                                  0   \n",
       "..                      ...                                ...   \n",
       "995                       0                                  0   \n",
       "996                       0                                  1   \n",
       "997                       1                                  0   \n",
       "998                       0                                  0   \n",
       "999                       1                                  0   \n",
       "\n",
       "     property_magnitude_no known property  property_magnitude_real estate  \n",
       "0                                       0                               1  \n",
       "1                                       0                               1  \n",
       "2                                       0                               1  \n",
       "3                                       0                               0  \n",
       "4                                       1                               0  \n",
       "..                                    ...                             ...  \n",
       "995                                     0                               1  \n",
       "996                                     0                               0  \n",
       "997                                     0                               0  \n",
       "998                                     1                               0  \n",
       "999                                     0                               0  \n",
       "\n",
       "[1000 rows x 38 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# 对数据进行标准化\n",
    "\n",
    "data_to_normalize = data[numeric_vars]\n",
    "\n",
    "normalized_data = scaler.fit_transform(data_to_normalize)\n",
    "\n",
    "# 将标准化后的数据更新回原始数据框中的对应列\n",
    "data[numeric_vars] = normalized_data\n",
    "data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二、箱线图分析\n",
    "筛选数值型特征绘制箱线图如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制箱线图\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.boxplot(data=data[numeric_vars])\n",
    "plt.title(\"Boxplot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察发现特征credit-amount存在较多的离群值，我们试图探究该离群情况是否与客户信用的类型相关。\n",
    "首先对credit-amount列数据创建分箱区间，进行分组统计，计算各分箱区间的频数，进而计算每个区间内信用分类“class”为好和坏的百分比。\n",
    "结果如下，可以看出在credit_amount较小的客户群体中，信用差的比例较低，随着credit_amount的增长，信用差的客户比例逐渐提升，说明该特征与客户信用好坏有较明显的关系，应重点关注。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     0    1          0          1\n",
      "credit_amount_bin                                \n",
      "(-1.077, 0.217]    189  549  25.609756  74.390244\n",
      "(0.217, 1.506]      68  109  38.418079  61.581921\n",
      "(1.506, 2.794]      23   34  40.350877  59.649123\n",
      "(2.794, 4.082]      16    6  72.727273  27.272727\n",
      "(4.082, 5.371]       4    2  66.666667  33.333333\n"
     ]
    }
   ],
   "source": [
    "# 创建分箱区间\n",
    "bins = pd.cut(data['credit_amount'], bins=5)\n",
    "\n",
    "# 将数据分箱并添加新列\n",
    "data['credit_amount_bin'] = bins\n",
    "# 分组统计并计算频数\n",
    "freq = data.groupby(['credit_amount_bin', 'class']).size().unstack()\n",
    "freq.columns.name = None  # 清除列名的索引名称\n",
    "\n",
    "# 计算每个分组内部的百分比\n",
    "percent = freq.div(freq.sum(axis=1), axis=0) * 100\n",
    "percent.columns.name = None  # 清除列名的索引名称\n",
    "\n",
    "# 合并结果\n",
    "result = pd.concat([freq, percent], axis=1)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、 相关性分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下图是编码后数据的相关系数矩阵图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>installment_commitment</th>\n",
       "      <th>residence_since</th>\n",
       "      <th>age</th>\n",
       "      <th>existing_credits</th>\n",
       "      <th>num_dependents</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.624984</td>\n",
       "      <td>0.074749</td>\n",
       "      <td>0.034067</td>\n",
       "      <td>-0.036136</td>\n",
       "      <td>-0.011284</td>\n",
       "      <td>-0.023834</td>\n",
       "      <td>-0.214927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>credit_amount</th>\n",
       "      <td>0.624984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.271316</td>\n",
       "      <td>0.028926</td>\n",
       "      <td>0.032716</td>\n",
       "      <td>0.020795</td>\n",
       "      <td>0.017142</td>\n",
       "      <td>-0.154739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>installment_commitment</th>\n",
       "      <td>0.074749</td>\n",
       "      <td>-0.271316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049302</td>\n",
       "      <td>0.058266</td>\n",
       "      <td>0.021669</td>\n",
       "      <td>-0.071207</td>\n",
       "      <td>-0.072404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence_since</th>\n",
       "      <td>0.034067</td>\n",
       "      <td>0.028926</td>\n",
       "      <td>0.049302</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.266419</td>\n",
       "      <td>0.089625</td>\n",
       "      <td>0.042643</td>\n",
       "      <td>-0.002967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>-0.036136</td>\n",
       "      <td>0.032716</td>\n",
       "      <td>0.058266</td>\n",
       "      <td>0.266419</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.149254</td>\n",
       "      <td>0.118201</td>\n",
       "      <td>0.091127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>existing_credits</th>\n",
       "      <td>-0.011284</td>\n",
       "      <td>0.020795</td>\n",
       "      <td>0.021669</td>\n",
       "      <td>0.089625</td>\n",
       "      <td>0.149254</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.109667</td>\n",
       "      <td>0.045732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>num_dependents</th>\n",
       "      <td>-0.023834</td>\n",
       "      <td>0.017142</td>\n",
       "      <td>-0.071207</td>\n",
       "      <td>0.042643</td>\n",
       "      <td>0.118201</td>\n",
       "      <td>0.109667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>-0.214927</td>\n",
       "      <td>-0.154739</td>\n",
       "      <td>-0.072404</td>\n",
       "      <td>-0.002967</td>\n",
       "      <td>0.091127</td>\n",
       "      <td>0.045732</td>\n",
       "      <td>0.003015</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        duration  credit_amount  installment_commitment  \\\n",
       "duration                1.000000       0.624984                0.074749   \n",
       "credit_amount           0.624984       1.000000               -0.271316   \n",
       "installment_commitment  0.074749      -0.271316                1.000000   \n",
       "residence_since         0.034067       0.028926                0.049302   \n",
       "age                    -0.036136       0.032716                0.058266   \n",
       "existing_credits       -0.011284       0.020795                0.021669   \n",
       "num_dependents         -0.023834       0.017142               -0.071207   \n",
       "class                  -0.214927      -0.154739               -0.072404   \n",
       "\n",
       "                        residence_since       age  existing_credits  \\\n",
       "duration                       0.034067 -0.036136         -0.011284   \n",
       "credit_amount                  0.028926  0.032716          0.020795   \n",
       "installment_commitment         0.049302  0.058266          0.021669   \n",
       "residence_since                1.000000  0.266419          0.089625   \n",
       "age                            0.266419  1.000000          0.149254   \n",
       "existing_credits               0.089625  0.149254          1.000000   \n",
       "num_dependents                 0.042643  0.118201          0.109667   \n",
       "class                         -0.002967  0.091127          0.045732   \n",
       "\n",
       "                        num_dependents     class  \n",
       "duration                     -0.023834 -0.214927  \n",
       "credit_amount                 0.017142 -0.154739  \n",
       "installment_commitment       -0.071207 -0.072404  \n",
       "residence_since               0.042643 -0.002967  \n",
       "age                           0.118201  0.091127  \n",
       "existing_credits              0.109667  0.045732  \n",
       "num_dependents                1.000000  0.003015  \n",
       "class                         0.003015  1.000000  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算数值型变量与分类目标变量之间的相关系数（皮尔逊相关系数）\n",
    "correlation_matrix = data[numeric_vars + [\"class\"]].corr()\n",
    "# 相关系数矩阵\n",
    "correlation_matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为更直观地展现数值型变量与分类变量之间的相关性，绘制热力图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 热力图\n",
    "correlation_matrix = data[numeric_vars + [\"class\"]].corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=None, cmap='coolwarm')\n",
    "plt.show()\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以直观地看到变量和class没有强相关性，其中duration和credit_amount是两个与class最相关的特征，它们的相关系数分别为-0.214927和-0.154739，说明它们与class呈负相关，即贷款期限和贷款金额越大，还款能力越差，这和人们的常识相符合。\n",
    "\n",
    "我们还尝试对类别型数据和分类目标变量进行卡方统计检验，结果显示，P值不显著。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chisq-statistic=24359.0477, p-value=1.0000, df=29940 expected_frep=[[0.91433394 0.70860881 1.08805739 ... 0.21212547 0.14080743 0.25692784]\n",
      " [0.91433394 0.70860881 1.08805739 ... 0.21212547 0.14080743 0.25692784]\n",
      " [0.84400056 0.65410044 1.00436067 ... 0.19580813 0.12997609 0.23716416]\n",
      " ...\n",
      " [0.98466732 0.76311718 1.17175411 ... 0.22844282 0.15163877 0.27669152]\n",
      " [0.77366718 0.59959207 0.92066395 ... 0.17949079 0.11914475 0.21740048]\n",
      " [0.98466732 0.76311718 1.17175411 ... 0.22844282 0.15163877 0.27669152]]\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "data.columns\n",
    "data2 = data\n",
    "data2=data2.loc[:, ~data.columns.isin (numeric_vars)]\n",
    "\n",
    "chi2_data = np.array(data2.iloc[1: , :-1])\n",
    "chi2_data\n",
    "chi2 = chi2_contingency(chi2_data)\n",
    "print('chisq-statistic=%.4f, p-value=%.4f, df=%i expected_frep=%s'%chi2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "综上，只凭相关性分析无法得到对于模型重要的特征，还需进一步分析，尝试计算属性和class之间的互信息。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四、互信息分析\n",
    "\n",
    "互信息是一种衡量两个变量之间相关性的指标，它表示了一个变量包含另一个变量信息的程度。互信息越大，说明两个变量越相关，互信息为0，说明两个变量相互独立。互信息对特征构成的作用主要如下：\n",
    "\n",
    "（1）如果一个特征和目标变量的互信息很大，那么这个特征可能对预测目标变量有很大的贡献，可以作为一个重要的特征。\n",
    "\n",
    "（2）如果一个特征和目标变量的互信息很小，那么这个特征可能对预测目标变量没有太大的影响，可以考虑删除或者降低其权重。\n",
    "\n",
    "（3）如果两个特征和目标变量的互信息都不大，但是它们之间又存在一定的互信息，那么它们可能存在一些非线性的交互效应，可以考虑构造一个新的特征来表示它们的组合。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Mutual Information:\n",
      "checking_status: 0.06829483270293979\n",
      "duration: 0.04410987441583458\n",
      "credit_history: 0.037302256711735815\n",
      "credit_amount: 0.03286699268692295\n",
      "savings_status: 0.02707033346857446\n",
      "employment: 0.02670793286981632\n",
      "installment_commitment: 0.024323503170824434\n",
      "residence_since: 0.019623524197581732\n",
      "age: 0.01950924658579911\n",
      "other_payment_plans: 0.018747404488666408\n",
      "housing: 0.018120651888727135\n",
      "existing_credits: 0.016910732800513584\n",
      "job: 0.015779237437556937\n",
      "num_dependents: 0.01569202729885366\n",
      "own_telephone: 0.012759542241626898\n",
      "foreign_worker: 0.012639897824802349\n",
      "purpose_business: 0.011782279164116005\n",
      "purpose_domestic appliance: 0.01079819997860687\n",
      "purpose_education: 0.009432157086506798\n",
      "purpose_furniture/equipment: 0.00939430095273086\n",
      "purpose_new car: 0.006238963263622166\n",
      "purpose_other: 0.004695370328037685\n",
      "purpose_radio/tv: 0.004348198974055029\n",
      "purpose_repairs: 0.003740706374470326\n",
      "purpose_retraining: 0.0018872754575518513\n",
      "purpose_used car: 0.0012685888750563201\n",
      "personal_status_female div/dep/mar: 0.00042015723394683846\n",
      "\n",
      "互信息不为0的属性个数为： 27\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import mutual_info_classif\n",
    "# 计算特征和目标的互信息\n",
    "mutual_info = mutual_info_classif(data[X.columns.tolist()],data[\"class\"])\n",
    "selected_features=[]\n",
    "print(\"\\nMutual Information:\")\n",
    "for i, info in enumerate(sorted(mutual_info,reverse=True)):\n",
    "    if info > 0:\n",
    "        print(f\"{X.columns[i]}: {info}\")\n",
    "        selected_features.append(f'{X.columns[i]}')\n",
    "selected_features\n",
    "print()\n",
    "print('互信息不为0的属性个数为：',len(selected_features))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图图显示了一个各个属性与class的互信息不为0的集合，可以看到checking_status和duration是两个与class最相关的特征，它们的互信息分别为0.0504260268800516和0.05037024403999246，说明它们包含了目标变量的较多信息，可以作为重要的特征。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 四、模型训练\n",
    "\n",
    "### 一、划分数据集\n",
    "\n",
    "首先将上述经过编码以及互信息筛选后的数据子集作为待划分数据集，并按1:9的比例划分为测试集和训练集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(900, 27)\n",
      "(100, 27)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "# 先将X设为互信息不为0的特征子集\n",
    "X=X[selected_features]\n",
    "# 将数据集分为0.9的训练集和0.1的测试集\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
    "# 打印各个数据集的形状\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 二、 模型训练与评估\n",
    "首先对模型进行权重调整，用平衡数据的不均衡性。代码中使用了两种权重方案，一种是1:1，即不做调整；另一种是0.84:1，即给少数类别的样本更高的权重。\n",
    "\n",
    "\n",
    "接着对六种不同的机器学习算法进行训练和评估，模型分别是：SVC、决策树、极度随机树、逻辑回归、随机森林和梯度提升树。对于每种算法，代码中使用了两种权重方案进行训练，并用测试集进行预测，使用准确率和F1两种指标对预测结果进行评估。。\n",
    "\n",
    "\n",
    "**性能指标**\n",
    "\n",
    "- 准确率 accuracy   \n",
    "- AUC\n",
    "\n",
    "**类别不平衡处理**\n",
    "\n",
    "根据前面对类别变量的类比占比统计结果，可以看到目标变量中good与bad的比例为**7：3**，训练数据存在不平衡，由于在实际应用中将信用不好的客户判断为好，可能带来较大的损失，应更关注较少数的信用不好的情况。\n",
    "\n",
    "因此，我们在模型拟合过程中对样本权重进行调整，将信用好的样本权重设为**0.84**，信用不好的样本权重设为**1**，这是一种正则化的方法，可以改变损失函数或评估指标对不同样本的敏感度，从而影响模型参数的更新方向和幅度。\n",
    "\n",
    "另外，也使得模型将更加关注信用不好的样本情况，从而提高模型性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入相关模型的包\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import ExtraTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from sklearn.utils.class_weight import compute_sample_weight\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1300x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 对模型进行权重调整\n",
    "class_weight = {1:1, 0:1}\n",
    "sample_weight_vect = compute_sample_weight(class_weight, y=y_train)\n",
    "models = [SVC(gamma='auto', probability=True),DecisionTreeClassifier(), \n",
    "          ExtraTreeClassifier(), LogisticRegression(), RandomForestClassifier(), \n",
    "          GradientBoostingClassifier()]\n",
    "# 训练每个算法模型并进行预测\n",
    "accuracies = []\n",
    "f1s=[]\n",
    "for model in models:\n",
    "    model.fit(X_train, y_train,sample_weight=sample_weight_vect)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracies.append(accuracy)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    f1s.append(f1)\n",
    "\n",
    "plt.figure(figsize=(13,8))\n",
    "plt.subplot(2,2,1)\n",
    "plt.bar(range(len(models)), accuracies)\n",
    "plt.xticks(range(len(models)), ['SVC','DT','ET','Lgr','RF','GB'])\n",
    "plt.title('Accuracy for Multiple Models (weight 1:1)', fontsize=20)\n",
    "# 在每个直方上方添加文本标签，显示高度\n",
    "for x, y in enumerate(accuracies):\n",
    "    plt.text(x - 0.15, y + 0.01, '%.3f' % y)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "plt.bar(range(len(models)), f1s)\n",
    "plt.xticks(range(len(models)), ['SVC','DT','ET','Lgr','RF','GB'])\n",
    "plt.title('f1 for Multiple Models (weight 1:1)', fontsize=20)\n",
    "for x, y in enumerate(f1s):\n",
    "    plt.text(x - 0.15, y + 0.01, '%.3f' % y)\n",
    "# 调整子图之间的间距\n",
    "plt.tight_layout()\n",
    "# 对模型进行权重调整\n",
    "class_weight = {1:0.84, 0:1}\n",
    "sample_weight_vect = compute_sample_weight(class_weight, y=y_train)\n",
    "models1 = [SVC(gamma='auto', probability=True),DecisionTreeClassifier(), ExtraTreeClassifier(), LogisticRegression(), RandomForestClassifier(), GradientBoostingClassifier()]\n",
    "# 训练每个算法模型并进行预测\n",
    "accuracies1 = []\n",
    "f1s1=[]\n",
    "for model in models1:\n",
    "    model.fit(X_train, y_train,sample_weight=sample_weight_vect)\n",
    "    y_pred = model.predict(X_test)\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    accuracies1.append(accuracy)\n",
    "    f1 = f1_score(y_test, y_pred)\n",
    "    f1s1.append(f1)\n",
    "    # print(model,':',accuracy)\n",
    "\n",
    "# 绘制准确率直方图\n",
    "plt.subplot(2,2,3)\n",
    "plt.bar(range(len(models1)), accuracies1)\n",
    "plt.xticks(range(len(models1)), ['SVC','DT','ET','Lgr','RF','GB'])\n",
    "plt.title('Accuracy for Multiple Models (weight 0.8:1)', fontsize=20)\n",
    "for x, y in enumerate(accuracies1):\n",
    "    plt.text(x - 0.15, y + 0.01, '%.3f' % y)\n",
    "\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "plt.bar(range(len(models)), f1s1)\n",
    "plt.xticks(range(len(models)), ['SVC','DT','ET','Lgr','RF','GB'])\n",
    "plt.title('f1 for Multiple Models (weight 0.8:1)', fontsize=20)\n",
    "for x, y in enumerate(f1s1):\n",
    "    plt.text(x - 0.15, y + 0.01, '%.3f' % y)\n",
    "\n",
    "# 调整子图之间的间距\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 创建新画布绘制每个模型的roc曲线\n",
    "plt.figure(figsize=(15,6), dpi=100)\n",
    "plt.subplot(1,2,1)\n",
    "colors = ['crimson', 'orange', 'gold', 'mediumseagreen', 'steelblue', 'mediumpurple']\n",
    "for model, color in zip(models, colors):\n",
    "    y_score = model.predict_proba(X_test)[:, 1] # 获取预测概率\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_score) # 计算fpr和tpr\n",
    "    auc_value = auc(fpr, tpr) # 计算auc值\n",
    "    plt.plot(fpr, tpr, lw=2, label=model.__class__.__name__ + ' (AUC = %.2f)' % auc_value, color=color) # 绘制roc曲线并设置标签\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=15)\n",
    "plt.ylabel('True Positive Rate', fontsize=15)\n",
    "plt.title('ROC Curve for Multiple Models (weight 1:1)', fontsize=20)\n",
    "plt.legend(loc='lower right', fontsize=12)\n",
    "\n",
    "# 创建新画布绘制每个模型的roc曲线\n",
    "plt.subplot(1,2,2)\n",
    "colors = ['crimson', 'orange', 'gold', 'mediumseagreen', 'steelblue', 'mediumpurple']\n",
    "for model, color in zip(models1, colors):\n",
    "    y_score = model.predict_proba(X_test)[:, 1] # 获取预测概率\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_score) # 计算fpr和tpr\n",
    "    auc_value = auc(fpr, tpr) # 计算auc值\n",
    "    plt.plot(fpr, tpr, lw=2, label=model.__class__.__name__ + ' (AUC = %.2f)' % auc_value, color=color) # 绘制roc曲线并设置标签\n",
    "plt.plot([0, 1], [0, 1], '--', color='gray')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=15)\n",
    "plt.ylabel('True Positive Rate', fontsize=15)\n",
    "plt.title('ROC Curve for Multiple Models (weight 0.8:1)', fontsize=20)\n",
    "plt.legend(loc='lower right', fontsize=12)\n",
    "plt.show\n",
    "# 保存或显示图像\n",
    "# plt.savefig('roc.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图（上）显示了两种权重方案下的六种算法模型在准确率和F1上的表现。从图中可以看出，权重调整对于不同的算法模型有不同的影响，有些模型在权重调整后表现得更好，有些则表现得更差。例如，SVC和逻辑回归在权重调整后准确率和F1都有所提高，而决策树和极度随机树则有所降低。另外，可以看出梯度提升树是表现最好的模型，无论是准确率还是F1分数都是最高的。\n",
    "\n",
    "\n",
    "图（下）中的ROC曲线显示了两种权重方案下的六种算法模型在不同阈值下的真正例率和假正例率，以及它们的AUC值。从图中可以看出，权重调整对于不同的算法模型也有不同的影响，有些模型在权重调整后ROC曲线更靠近左上角，有些则更靠近对角线。例如，SVC和逻辑回归在权重调整后AUC值都有所提高，而决策树和极度随机树则有所降低。另外，可以看出梯度提升树也是表现最好的模型，它的AUC值是最高的，说明该模型在区分正类和负类方面有很好的表现。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 三、 超参数的选择\n",
    "\n",
    "根据上述结果，本节使用10折交叉验证法和网格搜索对随机森林模型和梯度提升树模型进行超参数的选择。 \n",
    " \n",
    "首先使用10折交叉验证法对随机森林模型和梯度提升树模型进行评估。其中，10折交叉验将数据集分成10个子集，每次用一个子集作为测试集，其他子集作为训练集，重复10次，得到10个模型的评估指标，然后取平均值作为最终的评估结果。这样可以充分利用数据集的信息，避免因为数据划分的随机性而导致的模型评估结果不稳定的问题。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在测试集上模型的性能评估\n",
      "RF accuracy: 0.8000, AUC: 0.8541, F1:0.8630\n",
      "GB accuracy: 0.8100, AUC: 0.8626, F1:0.8690\n"
     ]
    }
   ],
   "source": [
    "# 使用RF模型进行训练和预测\n",
    "# 根据之前的结果，选择调整过比例的RF模型和未经调整比例的GB模型。\n",
    "rf = RandomForestClassifier()\n",
    "class_weight = {1:0.84, 0:1}\n",
    "sample_weight_vect = compute_sample_weight(class_weight, y=y_train)\n",
    "rf.fit(X_train, y_train,sample_weight=sample_weight_vect)\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "y_prob_rf = rf.predict_proba(X_test)[:,1]\n",
    "\n",
    "# 使用GB模型进行训练和预测\n",
    "gb = GradientBoostingClassifier()\n",
    "gb.fit(X_train, y_train)\n",
    "y_pred_gb = gb.predict(X_test)\n",
    "y_prob_gb = gb.predict_proba(X_test)[:,1]\n",
    "\n",
    "# 计算并打印评估指标\n",
    "acc_rf = accuracy_score(y_test, y_pred_rf)\n",
    "auc_rf = roc_auc_score(y_test, y_prob_rf)\n",
    "f1_rf = f1_score(y_test, y_pred_rf)\n",
    "acc_gb = accuracy_score(y_test, y_pred_gb)\n",
    "auc_gb = roc_auc_score(y_test, y_prob_gb)\n",
    "f1_gb = f1_score(y_test, y_pred_gb)\n",
    "print(\"在测试集上模型的性能评估\")\n",
    "print(f'RF accuracy: {acc_rf:.4f}, AUC: {auc_rf:.4f}, F1:{f1_rf:.4F}')\n",
    "print(f'GB accuracy: {acc_gb:.4f}, AUC: {auc_gb:.4f}, F1:{f1_gb:.4F}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "交叉验证评估模型性能\n",
      "RF accuracy: 0.7444, AUC: 0.7662\n",
      "GB accuracy: 0.7389,GB AUC: 0.7657\n",
      " \n",
      "网格搜索调整超参数\n",
      "Best RF parameters (accuracy): {'max_depth': None, 'max_features': 'log2', 'n_estimators': 150},Best RF accuracy: 0.7611\n",
      "Best RF parameters (AUC): {'max_depth': 10, 'max_features': 'auto', 'n_estimators': 100},Best RF AUC: 0.7770\n",
      "Best gb parameters (accuracy): {'max_depth': None, 'max_features': 'auto', 'n_estimators': 150}, Best gb accuracy: 0.7622\n",
      "Best gb parameters (AUC): {'max_depth': 5, 'max_features': 'sqrt', 'n_estimators': 100},Best gb AUC: 0.7825\n"
     ]
    }
   ],
   "source": [
    "# 使用交叉验证来评估RF模型的性能\n",
    "print('交叉验证评估模型性能')\n",
    "rf1 = RandomForestClassifier()\n",
    "acc_rf = cross_val_score(rf1, X_train, y_train, cv=10, scoring='accuracy')\n",
    "auc_rf = cross_val_score(rf1, X_train, y_train, cv=10, scoring='roc_auc')\n",
    "print(f'RF accuracy: {acc_rf.mean():.4f}, AUC: {auc_rf.mean():.4f}')\n",
    "\n",
    "# 使用交叉验证来评估GB模型的性能\n",
    "gb1 = GradientBoostingClassifier()\n",
    "acc_gb = cross_val_score(gb1, X_train, y_train, cv=10, scoring='accuracy')\n",
    "auc_gb = cross_val_score(gb1, X_train, y_train, cv=10, scoring='roc_auc')\n",
    "\n",
    "print(f'GB accuracy: {acc_gb.mean():.4f},GB AUC: {auc_gb.mean():.4f}')\n",
    "print(' ')\n",
    "print('网格搜索调整超参数')\n",
    "\n",
    "# 使用网格搜索来调整RF模型的超参数\n",
    "rf_params = {'n_estimators': [50, 100, 150 ], 'max_depth': [5, 10, 15, None ], 'max_features': ['auto', 'sqrt', 'log2']}\n",
    "rf_grid_acc = GridSearchCV(rf, rf_params, cv=10, scoring='accuracy')\n",
    "rf_grid_roc = GridSearchCV(rf, rf_params, cv=10, scoring='roc_auc')\n",
    "rf_grid_acc = rf_grid_acc.fit(X_train, y_train)\n",
    "rf_grid_roc = rf_grid_roc.fit(X_train, y_train)\n",
    "print(f'Best RF parameters (accuracy): {rf_grid_acc.best_params_},Best RF accuracy: {rf_grid_acc.best_score_:.4f}')\n",
    "print(f'Best RF parameters (AUC): {rf_grid_roc.best_params_},Best RF AUC: {rf_grid_roc.best_score_:.4f}')\n",
    "\n",
    "# 使用网格搜索来调整GB模型的超参数\n",
    "gb_params = {'n_estimators': [50, 100, 150 ], 'max_depth': [3, 5, 10, 15], 'learning_rate': [0.01, 0.1, 0.2]}\n",
    "gb_grid_acc = GridSearchCV(rf, rf_params, cv=10, scoring='accuracy')\n",
    "gb_grid_roc = GridSearchCV(rf, rf_params, cv=10, scoring='roc_auc')\n",
    "gb_grid_acc = gb_grid_acc.fit(X_train, y_train)\n",
    "gb_grid_roc = gb_grid_roc.fit(X_train, y_train)\n",
    "print(f'Best gb parameters (accuracy): {gb_grid_acc.best_params_}, Best gb accuracy: {gb_grid_acc.best_score_:.4f}')\n",
    "print(f'Best gb parameters (AUC): {gb_grid_roc.best_params_},Best gb AUC: {gb_grid_roc.best_score_:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接着，使用网格搜索优化随机森林模型和梯度提升树模型。其中，网格搜索是一种用来调整模型超参数的方法，它将每个超参数的可能取值组合成一个网格，然后对每个网格点上的参数组合进行模型训练和评估，最后选择评估结果最优的参数组合作为最优超参数。\n",
    "\n",
    "网格搜索可以简单地认为是在遍历给定的所有可能的参数组合并找到最优化模型性能的参数组合。首先需要定义随机森林和梯度提升树模型的超参数空间，即每个超参数的可能取值范围。超参数空间包括max_depth（树的最大深度）、n_estimators（树的数量）、max_features（构建每棵树时要考虑的特征数量）。接着，使用GridSearchCV函数来进行网格搜索和交叉验证，上图为调优结果。 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy——RF1: 0.8000, RF2: 0.7900, RF3: 0.7800\n",
      "AUC——RF1: 0.8541, RF2: 0.8441, RF2: 0.8616\n",
      "F1——RF1: 0.8630, RF2: 0.8696, RF2: 0.8533\n",
      "accuracy——GB1: 0.8100, GB2: 0.8441, GB3: 0.8300\n",
      "AUC——GB1: 0.8626, GB2: 0.7900, GB3: 0.8723\n",
      "F1——GB1: 0.8690, GB2: 0.8696, GB3: 0.8859\n"
     ]
    }
   ],
   "source": [
    "y_pred_gb = gb.predict(X_test)\n",
    "y_prob_gb = gb.predict_proba(X_test)[:,1]\n",
    "\n",
    "y_pred_gb_roc = gb_grid_roc.predict(X_test)\n",
    "y_prob_gb_roc = gb_grid_roc.predict_proba(X_test)[:,1]\n",
    "\n",
    "y_pred_gb_acc = gb_grid_acc.predict(X_test)\n",
    "y_prob_gb_acc = gb_grid_acc.predict_proba(X_test)[:,1]\n",
    "\n",
    "y_pred_rf = rf.predict(X_test)\n",
    "y_prob_rf = rf.predict_proba(X_test)[:,1]\n",
    "\n",
    "y_pred_rf_roc = gb_grid_roc.predict(X_test)\n",
    "y_prob_rf_roc = gb_grid_roc.predict_proba(X_test)[:,1]\n",
    "\n",
    "y_pred_rf_acc = rf_grid_acc.predict(X_test)\n",
    "y_prob_rf_acc = rf_grid_acc.predict_proba(X_test)[:,1]\n",
    "# 计算并打印准确率和AUC值\n",
    "acc_rf1 = accuracy_score(y_test, y_pred_rf)\n",
    "auc_rf1 = roc_auc_score(y_test, y_prob_rf)\n",
    "f1_rf1 = f1_score(y_test, y_pred_rf)\n",
    "acc_rf2 = accuracy_score(y_test, y_pred_rf_roc)\n",
    "auc_rf2 = roc_auc_score(y_test, y_prob_rf_roc)\n",
    "f1_rf2 = f1_score(y_test, y_pred_rf_roc)\n",
    "acc_rf3 = accuracy_score(y_test, y_pred_rf_acc)\n",
    "auc_rf3 = roc_auc_score(y_test, y_prob_rf_acc)\n",
    "f1_rf3 = f1_score(y_test, y_pred_rf_acc)\n",
    "\n",
    "acc_gb1 = accuracy_score(y_test, y_pred_gb)\n",
    "auc_gb1 = roc_auc_score(y_test, y_prob_gb)\n",
    "f1_gb1 = f1_score(y_test, y_pred_gb)\n",
    "auc_gb2 = accuracy_score(y_test, y_pred_gb_roc)\n",
    "acc_gb2 = roc_auc_score(y_test, y_prob_gb_roc)\n",
    "f1_gb2 = f1_score(y_test, y_pred_gb_roc)\n",
    "acc_gb3 = accuracy_score(y_test, y_pred_gb_acc)\n",
    "auc_gb3 = roc_auc_score(y_test, y_prob_gb_acc)\n",
    "f1_gb3 = f1_score(y_test, y_pred_gb_acc)\n",
    "\n",
    "print(f'accuracy——RF1: {acc_rf1:.4f}, RF2: {acc_rf2:.4f}, RF3: {acc_rf3:.4f}')\n",
    "print(f'AUC——RF1: {auc_rf1:.4f}, RF2: {auc_rf2:.4f}, RF2: {auc_rf3:.4f}')\n",
    "print(f'F1——RF1: {f1_rf1:.4f}, RF2: {f1_rf2:.4f}, RF2: {f1_rf3:.4f}')\n",
    "\n",
    "print(f'accuracy——GB1: {acc_gb1:.4f}, GB2: {acc_gb2:.4f}, GB3: {acc_gb3:.4f}')\n",
    "print(f'AUC——GB1: {auc_gb1:.4f}, GB2: {auc_gb2:.4f}, GB3: {auc_gb3:.4f}')\n",
    "print(f'F1——GB1: {f1_gb1:.4f}, GB2: {f1_gb2:.4f}, GB3: {f1_gb3:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 四、 最终模型特征分析\n",
    "#### 一、 特征重要性"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "特征重要性是一种用来评价特征对于模型预测能力的贡献的指标，它可以反映特征在模型中的相对重要性，越高的特征重要性说明越重要的特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GB=GradientBoostingClassifier(max_depth=10, max_features=None, n_estimators=100)\n",
    "GB.fit(X_train, y_train)\n",
    "importance = GB.feature_importances_\n",
    "# 获取数据集中的特征名称\n",
    "feature_names = X_train.columns\n",
    "# 可视化特征重要性\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.bar(range(len(importance)), sorted(importance,reverse=True), tick_label=feature_names)\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Importance')\n",
    "plt.title('Feature Importance')\n",
    "# 在每个直方上方添加文本标签，显示高度\n",
    "for x, y in enumerate(sorted(importance, reverse=True)):\n",
    " plt.text(x - 0.3 , y + 0.001, '%.3f' % y)\n",
    "\n",
    "# 调整子图之间的间距\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图示了梯度提升树模型中每个特征的重要性，并按照降序排列。从图中可以看出，checking_status是最重要的特征，它的重要性是0.201，远高于其他特征。其次是duration、credit_history和credit_amount，它们的重要性都在0.1左右。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 二、 交叉效应\n",
    "根据上述最重要的四个特征（checking_status， credit_history，savings_status，employment），下面分析它们与class之间的交叉效应，即不同类别的变量对class的分布情况的影响。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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eHh7Z1p2YmOhYb1aN9evXz9bv888/z1aft7e3PD09ndrsdrsuXLhww23Iuk7o3nvvven2XutzndPPxIkTJ1S0aFEFBgY6zZ/bz8itunq90uX9c/V/hlfLy8/giRMnFBwcnO36otKlS6to0aLZTkdf747Hl19+WWfOnHGcap4yZYruvfdetW/f/obrP3TokBo3bqyjR49q8uTJWrNmjeLj4zV16lRJyrbtOdlHWdt0tZwcx1OnTikjIyNHn7WrdevWTVOmTNFzzz2n//znP9qwYYPi4+NVqlQpp/ref/99DR06VN98842aN2+ugIAAdejQQb///ruky38ULFu2TK1atdKECRP04IMPqlSpUnrllVdu6bojq+KaHYtzc3NTixYt9P333+vIkSM5/mG81kWSgYGBSkhIyNZ+7NgxSXKcWy5atKgGDx6swYMH6/Tp01q6dKneeOMNtWrVSocPH3Zc/BcTE6OYmBgdOnRIixcv1uuvv66kpKSb3hl2O5QsWVJeXl7ZzvVfOV26fDdNRkaGEhMTc3XLek72zY1c6z+ixMRExy/zrDqHDRumTp06XXMZVapUuWmdLVq0kHR59CYuLk4RERGO9hEjRmj16tVKS0tzCjslS5ZUYGDgdY+fr6+vU41fffWVypcvf9NacivrL+wjR47c9Hq1a33ec/qZCAwM1KVLl3TixAmn/1RzGtwLSl5+BgMDA/Xzzz/LGOO075KSknTp0iXHvslyvYuuK1eurNatW2vq1Klq3bq1Fi9erNGjR8vNze2G6//mm2907tw5LVy40OkzlJcLcQMDA6/783UzAQEBcnNz05EjR25pncnJyfruu+80atQovf766472tLS0bN99VqxYMY0ePVqjR4/W8ePHHaM87dq10+7duyVdHunNuvj+t99+0xdffKHo6Gilp6drxowZt1Sb1TCycxcYNmyYjDF6/vnnlZ6enm36xYsX9e233950OS1atNDy5csd4SbLxx9/LG9v72ve2lq8eHE9+eST6t+/v06ePHnNL7ArV66cXnrpJUVERGjz5s053zAXatu2rfbu3avAwMBsI0b16tVz3E2VddHp9OnTb7i8nPxlnZN9c7XPPvtMxhjH+4MHD2rdunVq1qyZpMtBJiwsTNu2bbvmdtSrV88ROrIumLxWnWXKlFG1atX09ddfa9OmTY6wExERoT///FMTJ06Un5+f6tev75inbdu2OnHihDIyMq653qyQ1apVKxUtWlR79+69bo2u0LJlS7m5ud30WF1PTj8TzZs3lyTH6ESW+fPn52g9Ofms3G7X+xm8Xm0tWrTQ2bNn9c033zi1f/zxx47pOTVgwAD98ssv6tWrl9zc3PT888/fdJ6s8HTlRb/GGM2aNSvH671a8+bNtWPHDm3bts2pPSfHMevOyC+//DJHI6dZsi5wv/ri5dmzZysjI+O68wUFBal3797q2rWr9uzZo9TU1Gx97rvvPo0YMUI1a9YssN+rhQkjO3eBhg0bavr06YqMjFTdunX14osvqnr16rp48aK2bNmimTNnqkaNGtmui7jaqFGj9N1336l58+YaOXKkAgICNG/ePP3rX//ShAkT5O/vL0lq166d43twSpUqpYMHDyomJkbly5dXWFiYkpOT1bx5c3Xr1k1Vq1aVr6+v4uPj9cMPP1x3NOJq27dvz3Y3lnT5bqLcfI/JwIED9fXXX6tJkyYaNGiQatWqpczMTB06dEhLlizRkCFD9NBDD6lx48Z65plnNGbMGB0/flxt27aV3W7Xli1b5O3trZdfflmSVLNmTS1YsECff/65KlasKE9PT9WsWfOm++ZmkpKS1LFjRz3//PNKTk7WqFGj5OnpqWHDhjn6fPjhh2rdurVatWql3r1765577tHJkye1a9cubd68WV9++aUkOb55d+bMmfL19ZWnp6dCQ0MdoxMtWrTQBx98IC8vLzVq1EiSFBoaqtDQUC1ZskRPPPGEihb976+QLl26aN68eXr88cc1YMAANWjQQO7u7jpy5IhWrFih9u3bq2PHjqpQoYLefPNNDR8+XPv27dNjjz2mEiVK6Pjx49qwYYPjL9i8qlChgt544w299dZbOn/+vLp27Sp/f3/t3LlTf/31103XkdPPRMuWLdWkSRNFRUXp3Llzqlevnn788Ud98sknOaqzZs2aWrhwoaZPn666deuqSJEiLgt815PTn8Hr1dazZ09NnTpVvXr10oEDB1SzZk2tXbtW48aN0+OPP+404nczERERqlatmlasWKEePXqodOnSOZrHw8NDXbt2VVRUlC5cuKDp06fr1KlTudof0uXj/dFHH6lNmzYaM2aM426srFGTm5k4caIefvhhPfTQQ3r99ddVuXJlHT9+XIsXL9aHH37o+CPjSn5+fmrSpIneeecdlSxZUhUqVNCqVav0z3/+U8WLF3fq+9BDD6lt27aqVauWSpQooV27dumTTz5Rw4YN5e3trV9++UUvvfSSnnrqKYWFhcnDw0PLly/XL7/84jRqdNcqwIujkc+2bt1qevXqZcqVK2c8PDxMsWLFTJ06dczIkSOd7iC41t1KWX799VfTrl074+/vbzw8PMwDDzzgdIeHMca89957Jjw83JQsWdJ4eHiYcuXKmb59+5oDBw4YY4y5cOGC6devn6lVq5bx8/MzXl5epkqVKmbUqFHm3LlzN9yGG92NJcnMmjXL0fdW7sYyxpizZ8+aESNGmCpVqhgPDw/j7+9vatasaQYNGmQSExMd/TIyMsykSZNMjRo1HP0aNmxovv32W0efAwcOmJYtWxpfX18jyXFHy832zfVk3cXxySefmFdeecWUKlXK2O1207hxY7Nx48Zs/bdt22Y6d+5sSpcubdzd3U1wcLB55JFHzIwZM5z6xcTEmNDQUOPm5pbtbp3/+7//M5JMRESE0zzPP/+8kWTef//9bOu9ePGieffdd80DDzxgPD09jY+Pj6latap54YUXzO+//+7U95tvvjHNmzc3fn5+xm63m/Lly5snn3zSLF261NGnV69eplixYtnWM2rUKJPTX18ff/yxqV+/vqOeOnXqOG3n9T4nxuT8M3H69Gnz7LPPmuLFixtvb28TERFhdu/enaO7sU6ePGmefPJJU7x4cWOz2XK8XTeqvVevXk53UV19N1ZOfwZvVNuJEydMv379TJkyZUzRokVN+fLlzbBhw8yFCxecapFk+vfvf8PtiI6ONpLMTz/9lONt//bbbx2fs3vuuce89tprjjvjrrzbKaf7yBhjdu7caSIiIoynp6cJCAgwffv2dfwc3OxurKz5n3rqKRMYGOj4+e7du7djn1zrbqwjR46Yv/3tb6ZEiRLG19fXPPbYY2b79u2mfPnyplevXo5+r7/+uqlXr54pUaKEsdvtpmLFimbQoEHmr7/+MsYYc/z4cdO7d29TtWpVU6xYMePj42Nq1aplJk2aZC5dupTj/WpVNmOuGBMHACCf1atXTzabTfHx8QVdCiyK01gAgHyXkpKi7du367vvvtOmTZvuqGfz4c5D2AGAQiYjI0M3GnTP+rbwO9nmzZvVvHlzBQYGatSoUTyrDrcVp7EAoJBp1qyZVq1add3p5cuXz9HdewAuI+wAQCGzZ8+eG34RnN1uV82aNfOxIuDORtgBAACWxpcKAgAAS+MCZUmZmZk6duyYfH19r/u15gAAoHAxxujMmTMKCQlRkSLXH78h7Ojys51u9uwcAABQOB0+fPiGz34k7Oi/Dyg8fPiw/Pz8CrgaAACQEykpKSpbtuw1H8dxJcKO/vtQOT8/P8IOAAB3mJtdgsIFygAAwNIIOwAAwNIIOwAAwNK4ZgcAgDtYRkaGLl68WNBl3Bbu7u4ueQ4cYQcAgDuQMUaJiYk6ffp0QZdyWxUvXlzBwcF5+h48wg4AAHegrKBTunRpeXt7W+5LcY0xSk1NVVJSkiSpTJkyuV4WYQcAgDtMRkaGI+gEBgYWdDm3jZeXlyQpKSlJpUuXzvUpLS5QBgDgDpN1jY63t3cBV3L7ZW1jXq5LIuwAAHCHstqpq2txxTYSdgAAgKUVaNhZvXq12rVrp5CQENlsNn3zzTdO040xio6OVkhIiLy8vNSsWTPt2LHDqU9aWppefvlllSxZUsWKFdMTTzyhI0eO5ONWAABw5ztw4IBsNpu2bt1a0KW4XIGGnXPnzumBBx7QlClTrjl9woQJmjhxoqZMmaL4+HgFBwcrIiJCZ86ccfQZOHCgFi1apAULFmjt2rU6e/as2rZtq4yMjPzaDAAAUIgV6N1YrVu3VuvWra85zRijmJgYDR8+XJ06dZIkzZ07V0FBQZo/f75eeOEFJScn65///Kc++eQTPfroo5KkTz/9VGXLltXSpUvVqlWrfNsWAABQOBXaa3b279+vxMREtWzZ0tFmt9vVtGlTrVu3TpK0adMmXbx40alPSEiIatSo4egDAAD+KzMzU2+//bYqV64su92ucuXKaezYsdn6ZWRkqG/fvgoNDZWXl5eqVKmiyZMnO/VZuXKlGjRooGLFiql48eJq1KiRDh48KEnatm2bmjdvLl9fX/n5+alu3brauHFjvmzj1Qrt9+wkJiZKkoKCgpzag4KCHDsyMTFRHh4eKlGiRLY+WfNfS1pamtLS0hzvU1JSXFU2AACF2rBhwzRr1ixNmjRJDz/8sBISErR79+5s/TIzM3Xvvffqiy++UMmSJbVu3Tr9/e9/V5kyZdS5c2ddunRJHTp00PPPP6/PPvtM6enp2rBhg+Puqe7du6tOnTqaPn263NzctHXrVrm7u+f35koqxGEny9W3nBljbnob2s36jB8/XqNHj3ZJfbck2j//1+lq0ckFXYFrWOFYSNY4HhyLwoNjUbjc6Hj4lJUavSclnZeK5vzW7DNnz2ny5BhNGTNUvSJqSUpRpYrF9HDFujpw+P/fAJS0Wzpm5C5p9Asd/v+cpxXavJrWPdVGX8ydqc4PhynlVLKSk5PV9n+qqJJXiuQl3R9RS9IJ6dgJHTp4QK+99pqqVq0qSQoLC8vFTnCNQnsaKzg4WJKyjdAkJSU5RnuCg4OVnp6uU6dOXbfPtQwbNkzJycmO1+HDh11cPQAAhc+u3/crLS1dLR5ukKP+Mz7+SvVad1epmo/IJ6yRZs1fpEPHLv+/HFDCX707t1Or7v3VrtcATZ49XwnH/3TMO/jv3fXcc8/p0Ucf1f/+7/9q7969t2WbcqLQhp3Q0FAFBwcrLi7O0Zaenq5Vq1YpPDxcklS3bl25u7s79UlISND27dsdfa7FbrfLz8/P6QUAgNV5edpz3PeLxUs0aPR7evbp9lry2TRtXfKZ+nR+Qunp//0m4zmTRmv94liF13tAny9eovsad9RPm36RJEUP6acdO3aoTZs2Wr58uapVq6ZFixa5fJtyokDDztmzZ7V161bHPf379+/X1q1bdejQIdlsNg0cOFDjxo3TokWLtH37dvXu3Vve3t7q1q2bJMnf3199+/bVkCFDtGzZMm3ZskU9evRQzZo1HXdnAQCAy8JCy8nL01PL1m64ad81G7YovG4tRfburDo1qqpyaDntPZj9e+zq1KiqYS8/q3WLY1WjSiXN/+YHx7T77rtPgwYN0pIlS9SpUyfNmTPHpduTUwV6zc7GjRvVvHlzx/vBgwdLknr16qXY2FhFRUXp/PnzioyM1KlTp/TQQw9pyZIl8vX1dcwzadIkFS1aVJ07d9b58+fVokULxcbG5vphYQAAWJWnp11D+/dS1NjJ8nB3V6P6D+jPE6e047d92U5tVa5QVh9/9S/9Z+U6hZa9R598/S/Fb9up0LIhkqT9h45q5ryFeiKiiUKCS2nP3oP6bd8h9Xyyrc6fv6DXxsToyV4vKjQ0VEeOHFF8fLz+9re/FcRmF2zYadasmYwx151us9kUHR2t6Ojo6/bx9PTUBx98oA8++OA2VAgAgLX8Y+DzKurmppHvTtex43+qTOmS6vfMk9n69XvmSW3dsUdPv/i6bDaburZ/TJG9ntL3y3+UJHl7eWr3Hwc098tvdeJUssqULqmX+nTWC8/8TZcuZejEqWT17NlTx48fV8mSJdWpU6eCuTlIks3cKG3cJVJSUuTv76/k5OTbe/2OFe50uBvucriTWOF4cCwKD45F4XKD43HBp6z2N3pPofeUkuct3I2V70Lq5HkRFy5c0P79+xUaGipPT0+naTn9/7vQXqAMAADgCoQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaYQdAABgaQX6bCwAAOBaFd4/lm/rOvBKSK7mmzZtmt555x0lJCSoevXqiomJUePGjV1c3X8xsgMAAPLN559/roEDB2r48OHasmWLGjdurNatW+vQoUO3bZ2EHQAAkG8mTpyovn376rnnntP999+vmJgYlS1bVtOnT79t6yTsAACAfJGenq5NmzapZcuWTu0tW7bUunXrbtt6CTsAACBf/PXXX8rIyFBQUJBTe1BQkBITE2/begk7AAAgX9lsNqf3xphsba5E2AEAAPmiZMmScnNzyzaKk5SUlG20x5UIOwAAIF94eHiobt26iouLc2qPi4tTeHj4bVsv37MDAADyzeDBg/XMM8+oXr16atiwoWbOnKlDhw6pX79+t22dhB0AAJBvnn76aZ04cUJvvvmmEhISVKNGDf373/9W+fLlb9s6CTsAAFhIbr/VOD9FRkYqMjIy39bHNTsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSeFwEAABWMrNZ/q3r7ytveZbVq1frnXfe0aZNm5SQkKBFixapQ4cOLi/tSozsAACAfHPu3Dk98MADmjJlSr6tk5EdAACQb1q3bq3WrVvn6zoZ2QEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJbG3VgAACDfnD17Vn/88Yfj/f79+7V161YFBASoXLlyt2WdhB0AAKwkF1/0l582btyo5s2bO94PHjxYktSrVy/FxsbelnUSdgAAQL5p1qyZjDH5uk6u2QEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEA4E7z/y/wzefrfAuEKy5mJuwAAHCHcU87KWWkK/ViQVdy+6WmpkqS3N3dc70Mbj0HAOAO43YpVcUPfq8kjyclFZe3u2SzFXRV13DhQq5nNcYoNTVVSUlJKl68uNzc3HK9LMIOAAB3oODf50uSksq3ltw8Cria6zi3P8+LKF68uIKDg/O0DMIOAAB3IJuMyvw+T6X3LdRFz8DCObTz0sY8ze7u7p6nEZ0shB0AAO5gbhnn5XbuSEGXcW2engVdgSQuUAYAABZH2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZWqMPOpUuXNGLECIWGhsrLy0sVK1bUm2++qczMTEcfY4yio6MVEhIiLy8vNWvWTDt27CjAqgEAQGFSqMPO22+/rRkzZmjKlCnatWuXJkyYoHfeeUcffPCBo8+ECRM0ceJETZkyRfHx8QoODlZERITOnDlTgJUDAIDColCHnfXr16t9+/Zq06aNKlSooCeffFItW7bUxo2Xv37aGKOYmBgNHz5cnTp1Uo0aNTR37lylpqZq/vz5BVw9AAAoDAp12Hn44Ye1bNky/fbbb5Kkbdu2ae3atXr88cclSfv371diYqJatmzpmMdut6tp06Zat27ddZeblpamlJQUpxcAALCmQv1srKFDhyo5OVlVq1aVm5ubMjIyNHbsWHXt2lWSlJiYKEkKCgpymi8oKEgHDx687nLHjx+v0aNH377CAQBAoVGoR3Y+//xzffrpp5o/f742b96suXPn6t1339XcuXOd+tmuetKrMSZb25WGDRum5ORkx+vw4cO3pX4AAFDwCvXIzmuvvabXX39dXbp0kSTVrFlTBw8e1Pjx49WrVy8FBwdLujzCU6ZMGcd8SUlJ2UZ7rmS322W3229v8QAAoFAo1CM7qampKlLEuUQ3NzfHreehoaEKDg5WXFycY3p6erpWrVql8PDwfK0VAAAUToV6ZKddu3YaO3asypUrp+rVq2vLli2aOHGinn32WUmXT18NHDhQ48aNU1hYmMLCwjRu3Dh5e3urW7duBVw9AAAoDAp12Pnggw/0j3/8Q5GRkUpKSlJISIheeOEFjRw50tEnKipK58+fV2RkpE6dOqWHHnpIS5Yska+vbwFWDgAACotCHXZ8fX0VExOjmJiY6/ax2WyKjo5WdHR0vtUFAADuHIX6mh0AAIC8IuwAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLy3XY2bt3r0aMGKGuXbsqKSlJkvTDDz9ox44dLisOAAAgr4rmZqZVq1apdevWatSokVavXq2xY8eqdOnS+uWXXzR79mx99dVXrq4TAIC7SoUL8wu6hDw7UNAF/H+5Gtl5/fXXNWbMGMXFxcnDw8PR3rx5c61fv95lxQEAAORVrsLOr7/+qo4dO2ZrL1WqlE6cOJHnogAAAFwlV2GnePHiSkhIyNa+ZcsW3XPPPXkuCgAAwFVyFXa6deumoUOHKjExUTabTZmZmfrxxx/16quvqmfPni4t8OjRo+rRo4cCAwPl7e2t2rVra9OmTY7pxhhFR0crJCREXl5eatasGRdJAwAAh1yFnbFjx6pcuXK65557dPbsWVWrVk1NmjRReHi4RowY4bLiTp06pUaNGsnd3V3ff/+9du7cqffee0/Fixd39JkwYYImTpyoKVOmKD4+XsHBwYqIiNCZM2dcVgcAALhz5epuLHd3d82bN09vvvmmtmzZoszMTNWpU0dhYWEuLe7tt99W2bJlNWfOHEdbhQoVHP82xigmJkbDhw9Xp06dJElz585VUFCQ5s+frxdeeMGl9QAAgDtPnr5UsFKlSnryySfVuXNnlwcdSVq8eLHq1aunp556SqVLl1adOnU0a9Ysx/T9+/crMTFRLVu2dLTZ7XY1bdpU69atc3k9AADgzpOrkZ3Bgwdfs91ms8nT01OVK1dW+/btFRAQkKfi9u3bp+nTp2vw4MF64403tGHDBr3yyiuy2+3q2bOnEhMTJUlBQUFO8wUFBengwYPXXW5aWprS0tIc71NSUvJUJwAAKLxyFXa2bNmizZs3KyMjQ1WqVJExRr///rvc3NxUtWpVTZs2TUOGDNHatWtVrVq1XBeXmZmpevXqady4cZKkOnXqaMeOHZo+fbrThdA2m81pPmNMtrYrjR8/XqNHj851XQAA4M6Rq9NY7du316OPPqpjx45p06ZN2rx5s44ePaqIiAh17dpVR48eVZMmTTRo0KA8FVemTJlsYen+++/XoUOHJEnBwcGS5BjhyZKUlJRttOdKw4YNU3JysuN1+PDhPNUJAAAKr1yFnXfeeUdvvfWW/Pz8HG1+fn6Kjo7WhAkT5O3trZEjRzrdIp4bjRo10p49e5zafvvtN5UvX16SFBoaquDgYMXFxTmmp6ena9WqVQoPD7/ucu12u/z8/JxeAADAmnIVdpKTkx0P/7zSn3/+6bj+pXjx4kpPT89TcYMGDdJPP/2kcePG6Y8//tD8+fM1c+ZM9e/fX9Ll01cDBw7UuHHjtGjRIm3fvl29e/eWt7e3unXrlqd1AwAAa8jVNTvt27fXs88+q/fee0/169eXzWbThg0b9Oqrr6pDhw6SpA0bNui+++7LU3H169fXokWLNGzYML355psKDQ1VTEyMunfv7ugTFRWl8+fPKzIyUqdOndJDDz2kJUuWyNfXN0/rBgAA1pCrsPPhhx9q0KBB6tKliy5dunR5QUWLqlevXpo0aZIkqWrVqpo9e3aeC2zbtq3atm173ek2m03R0dGKjo7O87oAAID15Crs+Pj4aNasWZo0aZL27dsnY4wqVaokHx8fR5/atWu7qkYAAIBcy1XYyeLj46NatWq5qhYAAACXy3XYiY+P15dffqlDhw5luxB54cKFeS4MAADAFXJ1N9aCBQvUqFEj7dy5U4sWLdLFixe1c+dOLV++XP7+/q6uEQAAINdyNbIzbtw4TZo0Sf3795evr68mT56s0NBQvfDCCypTpoyrawQA5IMKF+YXdAkucaCgC0Chk6uRnb1796pNmzaSLn9B37lz52Sz2TRo0CDNnDnTpQUCAADkRa7CTkBAgM6cOSNJuueee7R9+3ZJ0unTp5Wamuq66gAAAPIoV6exGjdurLi4ONWsWVOdO3fWgAEDtHz5csXFxalFixaurhEAACDXchV2pkyZogsXLki6/FBNd3d3rV27Vp06ddI//vEPlxYIAACQF7kKOwEBAY5/FylSRFFRUYqKinJZUQAAAK6Sq2t23Nzcrvkg0BMnTsjNzS3PRQEAALhKrsKOMeaa7WlpafLw8MhTQQAAAK50S6ex3n//fUmXH745e/Zsp2dhZWRkaPXq1apataprKwQAAMiDWwo7WU80N8ZoxowZTqesPDw8VKFCBc2YMcO1FQIAAOTBLYWd/fv3S5KaN2+uhQsXqkSJErelKAAAAFfJ1d1YK1ascHUdAAAAt0Wuwk5GRoZiY2O1bNkyJSUlKTMz02n68uXLXVIcAABAXuUq7AwYMECxsbFq06aNatSoIZvN5uq6AAAAXCJXYWfBggX64osv9Pjjj7u6HgAAAJfK1ffseHh4qHLlyq6uBQAAwOVyFXaGDBmiyZMnX/fLBQEAAAqLXJ3GWrt2rVasWKHvv/9e1atXl7u7u9P0hQsXuqQ4AACAvMpV2ClevLg6duzo6loAAABcLldhZ86cOa6uAwAA4LbI1TU7knTp0iUtXbpUH374oc6cOSNJOnbsmM6ePeuy4gAAAPIqVyM7Bw8e1GOPPaZDhw4pLS1NERER8vX11YQJE3ThwgWejwUAAAqNXI3sDBgwQPXq1dOpU6fk5eXlaO/YsaOWLVvmsuIAAADyKtd3Y/3444/y8PBwai9fvryOHj3qksIAAABcIVcjO5mZmcrIyMjWfuTIEfn6+ua5KAAAAFfJVdiJiIhQTEyM473NZtPZs2c1atQoHiEBAAAKlVydxpo0aZKaN2+uatWq6cKFC+rWrZt+//13lSxZUp999pmrawQAAMi1XIWdkJAQbd26VQsWLNCmTZuUmZmpvn37qnv37k4XLAMAABS0XIUdSfLy8lKfPn3Up08fV9YDAADgUrm6Zmf8+PH66KOPsrV/9NFHevvtt/NcFAAAgKvkKux8+OGHqlq1arb26tWr84WCAACgUMlV2ElMTFSZMmWytZcqVUoJCQl5LgoAAMBVchV2ypYtqx9//DFb+48//qiQkJA8FwUAAOAqubpA+bnnntPAgQN18eJFPfLII5KkZcuWKSoqSkOGDHFpgQAAAHmRq7ATFRWlkydPKjIyUunp6ZIkT09PDR06VMOGDXNpgQAAAHlxy2EnIyNDa9eu1dChQ/WPf/xDu3btkpeXl8LCwmS3229HjQAAALl2y2HHzc1NrVq10q5duxQaGqr69evfjroAAABcIlcXKNesWVP79u1zdS0AAAAul6uwM3bsWL366qv67rvvlJCQoJSUFKcXAABAYZGrC5Qfe+wxSdITTzwhm83maDfGyGazKSMjwzXVAQAA5FGuws6KFStcXQcAAMBtkauw07RpU1fXAQAAcFvk6podSVqzZo169Oih8PBwHT16VJL0ySefaO3atS4rDgAAIK9yFXa+/vprtWrVSl5eXtq8ebPS0tIkSWfOnNG4ceNcWiAAAEBe5CrsjBkzRjNmzNCsWbPk7u7uaA8PD9fmzZtdVhwAAEBe5Srs7NmzR02aNMnW7ufnp9OnT+e1JgAAAJfJVdgpU6aM/vjjj2zta9euVcWKFfNcFAAAgKvkKuy88MILGjBggH7++WfZbDYdO3ZM8+bN06uvvqrIyEhX1wgAAJBruX7qeUpKipo3b64LFy6oSZMmstvtevXVV/XSSy+5ukYAAIBcu6Wwk5qaqtdee03ffPONLl68qHbt2mnIkCGSpGrVqsnHx+e2FAkAAJBbtxR2Ro0apdjYWHXv3l1eXl6aP3++MjMz9eWXX96u+gAAAPLklsLOwoUL9c9//lNdunSRJHXv3l2NGjVSRkaG3NzcbkuBAAAAeXFLFygfPnxYjRs3drxv0KCBihYtqmPHjrm8MAAAAFe4pbCTkZEhDw8Pp7aiRYvq0qVLLi0KAADAVW7pNJYxRr1795bdbne0XbhwQf369VOxYsUcbQsXLnRdhQAAAHlwS2GnV69e2dp69OjhsmIAAABc7ZbCzpw5c25XHTkyfvx4vfHGGxowYIBiYmIkXR5tGj16tGbOnKlTp07poYce0tSpU1W9evUCrRUAABQOufoG5YIQHx+vmTNnqlatWk7tEyZM0MSJEzVlyhTFx8crODhYEREROnPmTAFVCgAACpM7IuycPXtW3bt316xZs1SiRAlHuzFGMTExGj58uDp16qQaNWpo7ty5Sk1N1fz58wuwYgAAUFjcEWGnf//+atOmjR599FGn9v379ysxMVEtW7Z0tNntdjVt2lTr1q277vLS0tKUkpLi9AIAANaUq2dj5acFCxZo06ZN2rhxY7ZpiYmJkqSgoCCn9qCgIB08ePC6yxw/frxGjx7t2kIBAEChVKhHdg4fPqwBAwZo3rx58vT0vG4/m83m9N4Yk63tSsOGDVNycrLjdfjwYZfVDAAACpdCPbKzadMmJSUlqW7duo62jIwMrV69WlOmTNGePXskXR7hKVOmjKNPUlJSttGeK9ntdqfvCgIAANZVqEd2WrRooV9//VVbt251vOrVq6fu3btr69atqlixooKDgxUXF+eYJz09XatWrVJ4eHgBVg4AAAqLQj2y4+vrqxo1aji1FStWTIGBgY72gQMHaty4cQoLC1NYWJjGjRsnb29vdevWrSBKBgAAhUyhDjs5ERUVpfPnzysyMtLxpYJLliyRr69vQZcGAAAKgTsu7KxcudLpvc1mU3R0tKKjowukHgAAULgV6mt2AAAA8oqwAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwAALK1oQRdwN6lwYX5Bl5BnBwq6AAAAbhFhB0CBssIfARJ/CACFGaexAACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRXqsDN+/HjVr19fvr6+Kl26tDp06KA9e/Y49THGKDo6WiEhIfLy8lKzZs20Y8eOAqoYAAAUNoU67KxatUr9+/fXTz/9pLi4OF26dEktW7bUuXPnHH0mTJigiRMnasqUKYqPj1dwcLAiIiJ05syZAqwcAAAUFkULuoAb+eGHH5zez5kzR6VLl9amTZvUpEkTGWMUExOj4cOHq1OnTpKkuXPnKigoSPPnz9cLL7xQEGUDAIBCpFCP7FwtOTlZkhQQECBJ2r9/vxITE9WyZUtHH7vdrqZNm2rdunXXXU5aWppSUlKcXgAAwJrumLBjjNHgwYP18MMPq0aNGpKkxMRESVJQUJBT36CgIMe0axk/frz8/f0dr7Jly96+wgEAQIG6Y8LOSy+9pF9++UWfffZZtmk2m83pvTEmW9uVhg0bpuTkZMfr8OHDLq8XAAAUDoX6mp0sL7/8shYvXqzVq1fr3nvvdbQHBwdLujzCU6ZMGUd7UlJSttGeK9ntdtnt9ttXMAAAKDQK9ciOMUYvvfSSFi5cqOXLlys0NNRpemhoqIKDgxUXF+doS09P16pVqxQeHp7f5QIAgEKoUI/s9O/fX/Pnz9f//d//ydfX13Edjr+/v7y8vGSz2TRw4ECNGzdOYWFhCgsL07hx4+Tt7a1u3boVcPUAAKAwKNRhZ/r06ZKkZs2aObXPmTNHvXv3liRFRUXp/PnzioyM1KlTp/TQQw9pyZIl8vX1zedqAQBAYVSow44x5qZ9bDaboqOjFR0dffsLAgAAd5xCfc0OAABAXhF2AACApRF2AACApRF2AACApRXqC5SB26XChfkFXYJLHCjoAgDgDsDIDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTLhJ1p06YpNDRUnp6eqlu3rtasWVPQJQEAgELAEmHn888/18CBAzV8+HBt2bJFjRs3VuvWrXXo0KGCLg0AABQwS4SdiRMnqm/fvnruued0//33KyYmRmXLltX06dMLujQAAFDA7viwk56erk2bNqlly5ZO7S1bttS6desKqCoAAFBYFC3oAvLqr7/+UkZGhoKCgpzag4KClJiYeM150tLSlJaW5nifnJwsSUpJSbl9hUrKTEu9rcvPD7d7H+UXKxwLyRrHg2NReHAsChcrHI/bfSyylm+MuWG/Oz7sZLHZbE7vjTHZ2rKMHz9eo0ePztZetmzZ21KblfjHFHQFuBLHo/DgWBQeHIvCI7+OxZkzZ+Tv73/d6Xd82ClZsqTc3NyyjeIkJSVlG+3JMmzYMA0ePNjxPjMzUydPnlRgYOB1A9KdICUlRWXLltXhw4fl5+dX0OXc1TgWhQfHovDgWBQeVjkWxhidOXNGISEhN+x3x4cdDw8P1a1bV3FxcerYsaOjPS4uTu3bt7/mPHa7XXa73amtePHit7PMfOXn53dHf3ithGNReHAsCg+OReFhhWNxoxGdLHd82JGkwYMH65lnnlG9evXUsGFDzZw5U4cOHVK/fv0KujQAAFDALBF2nn76aZ04cUJvvvmmEhISVKNGDf373/9W+fLlC7o0AABQwCwRdiQpMjJSkZGRBV1GgbLb7Ro1alS2U3TIfxyLwoNjUXhwLAqPu+1Y2MzN7tcCAAC4g93xXyoIAABwI4QdAABgaYQdAABgaYQdAABgaYQdAADuQnfT/UmWufX8bnPkyBFNnz5d69atU2Jiomw2m4KCghQeHq5+/frxnC8AwA3Z7XZt27ZN999/f0GXcttx6/kdaO3atWrdurXKli2rli1bKigoSMYYJSUlKS4uTocPH9b333+vRo0aFXSpkHT48GGNGjVKH330UUGXclc4f/68Nm3apICAAFWrVs1p2oULF/TFF1+oZ8+eBVTd3WXXrl366aef1LBhQ1WtWlW7d+/W5MmTlZaWph49euiRRx4p6BLvClc+C/JKkydPVo8ePRQYGChJmjhxYn6Wla8IO3eg+vXr6+GHH9akSZOuOX3QoEFau3at4uPj87kyXMu2bdv04IMPKiMjo6BLsbzffvtNLVu21KFDh2Sz2dS4cWN99tlnKlOmjCTp+PHjCgkJ4Vjkgx9++EHt27eXj4+PUlNTtWjRIvXs2VMPPPCAjDFatWqV/vOf/xB48kGRIkX0wAMPZHsG5KpVq1SvXj0VK1ZMNptNy5cvL5gC8wFh5w7k5eWlrVu3qkqVKtecvnv3btWpU0fnz5/P58ruTosXL77h9H379mnIkCH8B5sPOnbsqEuXLmnOnDk6ffq0Bg8erO3bt2vlypUqV64cYScfhYeH65FHHtGYMWO0YMECRUZG6sUXX9TYsWMlScOHD1d8fLyWLFlSwJVa3/jx4zVr1izNnj3bKVy6u7tr27Zt2UZALcngjhMaGmo++uij607/6KOPTGhoaD5WdHez2WymSJEixmazXfdVpEiRgi7zrlC6dGnzyy+/OLVFRkaacuXKmb1795rExESORT7x8/Mzv//+uzHGmIyMDFO0aFGzadMmx/Rff/3VBAUFFVR5d50NGzaY++67zwwZMsSkp6cbY4wpWrSo2bFjRwFXlj+4QPkO9Oqrr6pfv37atGmTIiIiFBQUJJvNpsTERMXFxWn27NmKiYkp6DLvGmXKlNHUqVPVoUOHa07funWr6tatm79F3aXOnz+vokWdf61NnTpVRYoUUdOmTTV//vwCquzuVqRIEXl6ejqdRvH19VVycnLBFXWXqV+/vjZt2qT+/furbt26mjdvnmw2W0GXlW8IO3egyMhIBQYGatKkSfrwww8dQ/Jubm6qW7euPv74Y3Xu3LmAq7x71K1bV5s3b75u2LHZbHfVLZ4FqWrVqtq4cWO2u0s++OADGWP0xBNPFFBld58KFSrojz/+UOXKlSVJ69evV7ly5RzTDx8+7LiWCvnDx8dHc+fO1YIFCxQREXFXnc7lmp073MWLF/XXX39JkkqWLCl3d/cCrujus2bNGp07d06PPfbYNaefO3dOGzduVNOmTfO5srvP+PHjtWbNGv373/++5vTIyEjNmDFDmZmZ+VzZ3WfGjBkqW7as2rRpc83pw4cP1/HjxzV79ux8rgzS5a8v2bRpkx599FEVK1asoMu57Qg7AADA0vgGZQAAYGmEHQAAYGmEHQAAYGmEHQCFyoEDB2Sz2bR161ZJ0sqVK2Wz2XT69OlcL7NChQo3/DqGq9cJwFoIOwAKtfDwcCUkJMjf31+SFBsbm+1r7/OqbNmySkhIUI0aNW7al2AE3HkIOwBui4sXL7pkOR4eHgoODr6tX4Dm5uam4ODgbF9IeLu5ah8BuDHCDoAcy8zM1Ntvv63KlSvLbrerXLlyGjt2rGO044svvlCzZs3k6empTz/9VJI0Z84c3X///fL09FTVqlU1bdo0p2Vu2LBBderUkaenp+rVq6ctW7Y4Tb/yNNbKlSvVp08fJScny2azyWazKTo6Oke1p6am6tlnn5Wvr6/KlSunmTNnOqZdPVpz6tQpde/eXaVKlZKXl5fCwsI0Z84cSVJoaKgkqU6dOrLZbGrWrJlj37z55pu69957ZbfbVbt2bf3www/Z1nHlPpo5c6b8/Pz01VdfOdX67bffqlixYjpz5kyOtg3ATRTksyoA3FmioqJMiRIlTGxsrPnjjz/MmjVrzKxZs8z+/fuNJFOhQgXz9ddfm3379pmjR4+amTNnmjJlyjjavv76axMQEGBiY2ONMcacPXvWlCpVyjz99NNm+/bt5ttvvzUVK1Y0ksyWLVuMMcasWLHCSDKnTp0yaWlpJiYmxvj5+ZmEhASTkJBgzpw5c9O6y5cvbwICAszUqVPN77//bsaPH2+KFClidu3aZYwxjvqz1tm/f39Tu3ZtEx8fb/bv32/i4uLM4sWLjTGXnzEkySxdutQkJCSYEydOGGOMmThxovHz8zOfffaZ2b17t4mKijLu7u7mt99+c1rH1fvo+eefN48//rhTvR07djQ9e/bM8/ECcBlhB0COpKSkGLvdbmbNmpVtWtZ/5DExMU7tZcuWNfPnz3dqe+utt0zDhg2NMcZ8+OGHJiAgwJw7d84xffr06dcNO8YYM2fOHOPv739LtZcvX9706NHD8T4zM9OULl3aTJ8+3an+rHW2a9fO9OnT55rLurpvlpCQEDN27Fintvr165vIyEin+a7eRz///LNxc3MzR48eNcYY8+effxp3d3ezcuXKW9pGANfHaSwAObJr1y6lpaWpRYsW1+1Tr149x7///PNPHT58WH379pWPj4/jNWbMGO3du9exzAceeEDe3t6O+Ro2bHhb6q9Vq5bj3zabTcHBwUpKSrpm3xdffFELFixQ7dq1FRUVpXXr1t1w2SkpKTp27JgaNWrk1N6oUSPt2rXLqe3KfSRJDRo0UPXq1fXxxx9Lkj755BOVK1dOTZo0yfG2Abgxwg6AHPHy8rppnyufsZP1/KlZs2Zp69atjtf27dv1008/SVK+PiD16ufG2Wy26z4jq3Xr1jp48KAGDhyoY8eOqUWLFnr11Vdvuo6rL6I2xmRru9ZziJ577jnHNUFz5sxRnz597qonUgO3G2EHQI6EhYXJy8tLy5Yty1H/oKAg3XPPPdq3b58qV67s9Mq6yLdatWratm2bzp8/75gvKwhdj4eHR748rblUqVLq3bu3Pv30U8XExDguaPbw8JAkpxr8/PwUEhKitWvXOi1j3bp12Z7Afi09evTQoUOH9P7772vHjh3q1auXC7cEQP7eZwngjuXp6amhQ4cqKipKHh4eatSokf7880/t2LHjuqe2oqOj9corr8jPz0+tW7dWWlqaNm7cqFOnTmnw4MHq1q2bhg8frr59+2rEiBE6cOCA3n333RvWUaFCBZ09e1bLli1znAK78jSYK4wcOVJ169ZV9erVlZaWpu+++84RWkqXLi0vLy/98MMPuvfee+Xp6Sl/f3+99tprGjVqlCpVqqTatWtrzpw52rp1q+bNm3fT9ZUoUUKdOnXSa6+9ppYtW+ree+916fYAdztGdgDk2D/+8Q8NGTJEI0eO1P3336+nn376ute9SJdPz8yePVuxsbGqWbOmmjZtqtjYWMfIjo+Pj7799lvt3LlTderU0fDhw/X222/fsIbw8HD169dPTz/9tEqVKqUJEya4dBuly6M3w4YNU61atdSkSRO5ublpwYIFkqSiRYvq/fff14cffqiQkBC1b99ekvTKK69oyJAhGjJkiGrWrKkffvhBixcvVlhYWI7W2bdvX6Wnp+vZZ591+fYAdzubyc+T5gCAa5o3b54GDBigY8eOOU6VAXANTmMBQAFKTU3V/v37NX78eL3wwgsEHeA24DQWgDvamjVrnG5tv/pV2E2YMEG1a9dWUFCQhg0bVtDlAJbEaSwAd7Tz58/r6NGj151euXLlfKwGQGFE2AEAAJbGaSwAAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBphB0AAGBp/w/BYTvivy/AbAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rWbOmGjdu7PRq1KjRdW3jpY4cOaJvv/1W7u7uTi8/Pz8ZYxzn148ePSo3NzeFhITkaz3X8tlcSV7rDQkJcXz2R44ckSSNHDky17ZER0dLUq5rBfLSrl07JSUl6aefftKqVavUsGFDlS1bVm3atNGqVauUnp6ujRs3ql27do55jhw5ohMnTsjDwyPXulNSUhzrzanxzjvvzNVv8eLFuerz9vbO9QvGbrcrIyPjitvgivV4eHg4RvH9uT2v9V9t/+Tl2LFjKlmypMqUKePUbrPZcs3btWtXVaxYUa+99pqki9cKnTlzJs/w/ee6PTw8rtiesz05n9uDDz6Y63N76aWXZIzJdauKoKAgp/c51wamp6dfdruvZMuWLWrfvr2kiyNJv/76ayUmJmrs2LF5LvfP68+p4dJ+x44du+z+uZqca85uv/32a98IXbyurX379lqyZIlGjRql1atXa8uWLdq8eXOu7bjad2KpUqW0fv16NWjQQM8//7xq166tcuXKafz48df9x+KtgNFYtxg3Nze1bdtWy5cv16+//nrN/1kv/csvR1BQkA4fPpyr/ffff5d0MVhJF/8iHj58uIYPH64TJ05o1apVev7559WhQwclJyfL29tbpUuXVlxcnOLi4pSUlKRPP/1Uzz33nFJTU686MuxGKF26tLy8vPK8qDFnuiSVKVNGWVlZSklJydeQ9Wv5bK4krzCYkpLi+LLPqXPMmDHq3r17nsuoXr36Vets27atpItHbxISEhQVFeVof+GFF/TVV18pMzPTKeyULl1aQUFBl91/fn5+TjV+9NFHjr9wb4TCWs+lLrd/qlSpctl5goKCdOHCBR09etQp8BhjlJKSojvvvNPRVqJECQ0aNEjPP/+8Xn31Vc2aNUtt27a9pn16rXI+txkzZuiuu+7Ks8+lR0duhEWLFsnd3V2ff/65UwAtyH16goKCLrt/riZnv1zvhcDff/+9du3apfj4ePXp08fRntcF4dfynVi3bl0tWrRIxhh9++23io+P14svvigvLy8999xz11Wb1XFk5xY0ZswYGWP017/+VefOncs1/fz58/rss8+uupy2bdtqzZo1jnCT4+2335a3t3eeX4wBAQF68MEHNWjQIP3xxx953myrQoUKGjx4sKKiorR9+/Zr3zAX6ty5s/bv36+goKBcR4waN27sGE3VsWNHSdLs2bOvuLw//1WZl2v5bP7s/fffdxqNcujQIW3cuNExsqV69eqqWrWqdu3aled2NG7c2BE6rvTXd2hoqGrVqqWPP/5Y27Ztc4SdqKgoHT16VFOnTpW/v7/TL+LOnTvr2LFjysrKynO9Ob+QO3TooJIlS2r//v2XrdEVCms9l3rvvfec3m/cuFGHDh264sijnGD57rvvOrV//PHHOnPmjGN6jv79+8vDw0O9evXSDz/8oMGDB7um+P/XokULBQQEaM+ePZf93HKOBl2Pa/k/kcNms6lkyZJyc3NztKWnp+udd9657vXmaN26tVavXu04ciVJWVlZWrx48VXnrVatmipXrqx58+Zd8yhA6X9/NP55FOzrr79+xfmu9p1os9lUv359TZs2TQEBAUX2vVmccWTnFtSsWTPNnj1b0dHRatSokZ5++mnVrl1b58+f144dOzR37lzVqVMn13URfzZ+/Hh9/vnnat26tcaNG6fAwEC99957+uKLLzRlyhSVKlVKktSlSxfHfXDKlCmjQ4cOKS4uTuHh4apatarS0tLUunVr9ezZUzVq1JCfn58SExO1YsWKyx6N+LPvv/9eFy5cyNVeuXLlXKcDrsXQoUP18ccf65577tGwYcNUr149ZWdnKykpSStXrtSIESPUtGlTtWzZUo8//rgmTpyoI0eOqHPnzrLb7dqxY4e8vb31zDPPSPrfX2CLFy9WpUqV5Onpqbp16171s7ma1NRUPfDAA/rrX/+qtLQ0jR8/Xp6enhozZoyjz+uvv66OHTuqQ4cO6tu3r8qXL68//vhDe/fu1fbt2/Xhhx9KunivIkmaO3eu/Pz85OnpqYiICMdRorZt22rGjBny8vJSixYtJEkRERGKiIjQypUrdf/996tkyf99pTzyyCN67733dN9992nIkCFq0qSJ3N3d9euvv2rt2rXq2rWrHnjgAVWsWFEvvviixo4dq19++UX33nuvbrvtNh05ckRbtmyRj4+P41YHBVFY67nU1q1b1b9/fz300ENKTk7W2LFjVb58eccpxLxERUWpQ4cOGj16tE6ePKkWLVro22+/1fjx49WwYUM9/vjjTv0DAgLUu3dvzZ49W+Hh4Vf9f3u9fH19NWPGDPXp00d//PGHHnzwQZUtW1ZHjx7Vrl27dPTo0auG/bzUrVtX69at02effabQ0FD5+fld9ohUp06dNHXqVPXs2VNPPfWUjh07pldeeSXPW2dcqxdeeEGffvqp2rRpo3Hjxsnb21uvvfZaruuPLue1115Tly5ddNddd2nYsGGqUKGCkpKS9OWXX+YKuTlq1KihypUr67nnnpMxRoGBgfrss88c173luJbvxM8//1yzZs1St27dVKlSJRljtGTJEp04ccLxxwguUXTXRqOo7dy50/Tp08dUqFDBeHh4OIa2jhs3zqSmpjr65TVaKcd3331nunTpYkqVKmU8PDxM/fr1HcOZc7z66qumefPmpnTp0sbDw8NUqFDB9OvXzxw8eNAYc3Fo+MCBA029evWMv7+/8fLyMtWrVzfjx483Z86cueI2XGk0lv40LPd6RmMZc3Fo5wsvvGCqV69uPDw8TKlSpUzdunXNsGHDnEZwZGVlmWnTppk6deo4+jVr1sx89tlnjj4HDx407du3N35+fo5hwNfy2VxOzmisd955x/ztb38zZcqUMXa73bRs2dJpCHSOXbt2mR49epiyZcsad3d3ExISYtq0aWPmzJnj1C8uLs5EREQYNzc3p6HpxhjzySefGEkmKirKaZ6//vWvRpL517/+lWu958+fN6+88oqpX7++8fT0NL6+vqZGjRpmwIAB5qeffnLqu2zZMtO6dWvj7+9v7Ha7CQ8PNw8++KDT0OY+ffoYHx+fXOvJGQV0LQqynmv9Gcr5uVy5cqV5/PHHTUBAgGOo9Z+3+8+jsYy5OHpo9OjRJjw83Li7u5vQ0FDz9NNPOw1Zv9S6deuMJPPPf/4zz+mSzKBBg5zaDhw4YCSZl19+2ak952frww8/dGpfv3696dSpkwkMDDTu7u6mfPnyplOnTk79cvbD0aNHnebNa1TSzp07TYsWLYy3t7eRlOdoxUvNmzfPVK9e3djtdlOpUiUTGxtr3nrrrVzLvdz/58jIyFzr+Prrr81dd91l7Ha7CQkJMc8++6yZO3fuNY3GMubiEPqOHTuaUqVKGbvdbipXrmyGDRt2xe3es2ePiYqKMn5+fua2224zDz30kElKSjKSzPjx440x1/aduG/fPvPoo4+aypUrGy8vL1OqVCnTpEkTEx8ff9W6b0U2Y/50RyYAQIHEx8friSeeUGJi4g05PfZnI0aM0OzZs5WcnJznxbnArY7TWABwk9q8ebN+/PFHzZo1SwMGDCDoAJdB2AGAm1SzZs3k7e2tzp07a+LEiUVdDlBscRoLAABYGkPPAQCApRF2AACApRF2AACApXGBsi4+r+T333+Xn59fno9FAAAAxY8xRqdOnVK5cuVUosTlj98QdnTxWU5/fnI3AAC4OSQnJ1/xWY+EHf3vgYTJycny9/cv4moAAMC1OHnypMLCwhy/xy+HsKP/PZzN39+fsAMAwE3mapegcIEyAACwNMIOAACwNMIOAACwNK7ZAQDgJpaVlaXz588XdRk3hLu7u9zc3Aq8HMIOAAA3IWOMUlJSdOLEiaIu5YYKCAhQSEhIge6DR9gBAOAmlBN0ypYtK29vb8vdFNcYo7Nnzyo1NVWSFBoamu9lEXYAALjJZGVlOYJOUFBQUZdzw3h5eUmSUlNTVbZs2Xyf0uICZQAAbjI51+h4e3sXcSU3Xs42FuS6JMIOAAA3KaudusqLK7aRsAMAACytSMPOV199pS5duqhcuXKy2WxatmyZ03RjjGJiYlSuXDl5eXmpVatW2r17t1OfzMxMPfPMMypdurR8fHx0//3369dffy3ErQAA4OZ38OBB2Ww27dy5s6hLcbkiDTtnzpxR/fr1NXPmzDynT5kyRVOnTtXMmTOVmJiokJAQRUVF6dSpU44+Q4cO1dKlS7Vo0SJt2LBBp0+fVufOnZWVlVVYmwEAAIqxIh2N1bFjR3Xs2DHPacYYxcXFaezYserevbskacGCBQoODtbChQs1YMAApaWl6a233tI777yjdu3aSZLeffddhYWFadWqVerQoUOhbQsAACieiu01OwcOHFBKSorat2/vaLPb7YqMjNTGjRslSdu2bdP58+ed+pQrV0516tRx9AEAAP+TnZ2tl156SVWqVJHdbleFChU0adKkXP2ysrLUr18/RUREyMvLS9WrV9f06dOd+qxbt05NmjSRj4+PAgIC1KJFCx06dEiStGvXLrVu3Vp+fn7y9/dXo0aNtHXr1kLZxj8rtvfZSUlJkSQFBwc7tQcHBzs+yJSUFHl4eOi2227L1Sdn/rxkZmYqMzPT8f7kyZOuKhsAgGJtzJgxeuONNzRt2jTdfffdOnz4sPbt25erX3Z2tm6//XZ98MEHKl26tDZu3KinnnpKoaGh6tGjhy5cuKBu3brpr3/9q95//32dO3dOW7ZscYye6tWrlxo2bKjZs2fLzc1NO3fulLu7e2FvrqRiHHZy/HnImTHmqsPQrtYnNjZWEyZMcEl91yWmVOGv09Vi0oq6Atewwr6QrLE/2BfFB/uieLnS/vANk1q8KqWmSyWvfWj2qdNnNH16nGZOHK0+UfUknVTlSj66u1IjHUz+/wFAqfuk343cJU0Y0O3/5zyhiNa1tPGhTvpgwVz1uLuqTh5PU1pamjrfVV2VvU5KXlLNqHqSjkm/H1PSoYN69tlnVaNGDUlS1apV8/EhuEaxPY0VEhIiSbmO0KSmpjqO9oSEhOjcuXM6fvz4ZfvkZcyYMUpLS3O8kpOTXVw9AADFz96fDigz85za3t3kmvrPefsjNe7YS2XqtpFv1RZ6Y+FSJf1+8fdy4G2l1LdHF3XoNUhd+gzR9DcX6vCRo455hz/VS/3791e7du30z3/+U/v3778h23Qtim3YiYiIUEhIiBISEhxt586d0/r169W8eXNJUqNGjeTu7u7U5/Dhw/r+++8dffJit9vl7+/v9AIAwOq8PO3X3PeDT1dq2IRX9eTDXbXy/VnaufJ9PdHjfp079787Gc+fNkGbPo1X88b1tfjTlarW8gFt3vatJClmxEDt3r1bnTp10po1a1SrVi0tXbrU5dt0LYo07Jw+fVo7d+50jOk/cOCAdu7cqaSkJNlsNg0dOlSTJ0/W0qVL9f3336tv377y9vZWz549JUmlSpVSv379NGLECK1evVo7duzQY489prp16zpGZwEAgIuqRlSQl6enVm/YctW+/9myQ80b1VN03x5qWKeGqkRU0P5Due9j17BODY155klt/DRedapX1sJlKxzTqlWrpmHDhmnlypXq3r275s+f79LtuVZFes3O1q1b1bp1a8f74cOHS5L69Omj+Ph4jRo1Sunp6YqOjtbx48fVtGlTrVy5Un5+fo55pk2bppIlS6pHjx5KT09X27ZtFR8fn++HhQEAYFWennaNHtRHoyZNl4e7u1rcWV9Hjx3X7h9/yXVqq0rFML390Rf6ct1GRYSV1zsff6HEXXsUEVZOknQg6TfNfW+J7o+6R+VCyuiH/Yf04y9J6v1gZ6WnZ+jZiXF6sM/TioiI0K+//qrExET95S9/KYrNLtqw06pVKxljLjvdZrMpJiZGMTExl+3j6empGTNmaMaMGTegQgAArOXvQ/+qkm5uGvfKbP1+5KhCy5bWwMcfzNVv4OMPaufuH/Tw08/JZrPp0a73KrrPQ1q+5mtJkreXp/b9fFALPvxMx46nKbRsaQ1+oocGPP4XXbiQpWPH09S7d28dOXJEpUuXVvfu3YtmcJAkm7lS2rhFnDx5UqVKlVJaWtqNvX7HCiMdboVRDjcTK+wP9kXxwb4oXq6wPzJ8w3SgxauKKF9GntcxGqvQlWtY4EVkZGTowIEDioiIkKenp9O0a/39XWwvUAYAAHAFwg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALA0wg4AALC0In02FgAAcK2K//q90NZ18G/l8jXfrFmz9PLLL+vw4cOqXbu24uLi1LJlSxdX9z8c2QEAAIVm8eLFGjp0qMaOHasdO3aoZcuW6tixo5KSkm7YOgk7AACg0EydOlX9+vVT//79VbNmTcXFxSksLEyzZ8++Yesk7AAAgEJx7tw5bdu2Te3bt3dqb9++vTZu3HjD1kvYAQAAheK///2vsrKyFBwc7NQeHByslJSUG7Zewg4AAChUNpvN6b0xJlebKxF2AABAoShdurTc3NxyHcVJTU3NdbTHlQg7AACgUHh4eKhRo0ZKSEhwak9ISFDz5s1v2Hq5zw4AACg0w4cP1+OPP67GjRurWbNmmjt3rpKSkjRw4MAbtk7CDgAAKDQPP/ywjh07phdffFGHDx9WnTp19O9//1vh4eE3bJ2EHQAALCS/dzUuTNHR0YqOji609XHNDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDQeFwEAgJXMbVV463pq3XXP8tVXX+nll1/Wtm3bdPjwYS1dulTdunVzeWmX4sgOAAAoNGfOnFH9+vU1c+bMQlsnR3YAAECh6dixozp27Fio6+TIDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDTCDgAAsDRGYwEAgEJz+vRp/fzzz473Bw4c0M6dOxUYGKgKFSrckHUSdgAAQKHZunWrWrdu7Xg/fPhwSVKfPn0UHx9/Q9ZJ2AEAwErycVfjwtSqVSsZYwp1nVyzAwAALI2wAwAALI2wAwAALI2wAwAALI2wAwDAzeb/L/At5Ot8i4QrLmYm7AAAcJNxz/xDyjqns+eLupIb7+zZs5Ikd3f3fC+DoecAANxk3C6cVcCh5Ur1eFBSgLzdJZutqKvKQ0ZGvmc1xujs2bNKTU1VQECA3Nzc8r0swg4AADehkJ8WSpJSwztKbh5FXM1lnDlQ4EUEBAQoJCSkQMsg7AAAcBOyySj0p/dU9pclOu8ZVDwP7QzeWqDZ3d3dC3REJwdhBwCAm5hbVrrczvxa1GXkzdOzqCuQxAXKAADA4gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0op12Llw4YJeeOEFRUREyMvLS5UqVdKLL76o7OxsRx9jjGJiYlSuXDl5eXmpVatW2r17dxFWDQAAipNiHXZeeuklzZkzRzNnztTevXs1ZcoUvfzyy5oxY4ajz5QpUzR16lTNnDlTiYmJCgkJUVRUlE6dOlWElQMAgOKiWIedTZs2qWvXrurUqZMqVqyoBx98UO3bt9fWrRdvP22MUVxcnMaOHavu3burTp06WrBggc6ePauFCxcWcfUAAKA4KNZh5+6779bq1av1448/SpJ27dqlDRs26L777pMkHThwQCkpKWrfvr1jHrvdrsjISG3cuPGyy83MzNTJkyedXgAAwJqK9bOxRo8erbS0NNWoUUNubm7KysrSpEmT9Oijj0qSUlJSJEnBwcFO8wUHB+vQoUOXXW5sbKwmTJhw4woHAADFRrE+srN48WK9++67WrhwobZv364FCxbolVde0YIFC5z62f70pFdjTK62S40ZM0ZpaWmOV3Jy8g2pHwAAFL1ifWTn2Wef1XPPPadHHnlEklS3bl0dOnRIsbGx6tOnj0JCQiRdPMITGhrqmC81NTXX0Z5L2e122e32G1s8AAAoFor1kZ2zZ8+qRAnnEt3c3BxDzyMiIhQSEqKEhATH9HPnzmn9+vVq3rx5odYKAACKp2J9ZKdLly6aNGmSKlSooNq1a2vHjh2aOnWqnnzySUkXT18NHTpUkydPVtWqVVW1alVNnjxZ3t7e6tmzZxFXDwAAioNiHXZmzJihv//974qOjlZqaqrKlSunAQMGaNy4cY4+o0aNUnp6uqKjo3X8+HE1bdpUK1eulJ+fXxFWDgAAiotiHXb8/PwUFxenuLi4y/ax2WyKiYlRTExModUFAABuHsX6mh0AAICCIuwAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLI+wAAABLy3fY2b9/v1544QU9+uijSk1NlSStWLFCu3fvdllxAAAABZWvsLN+/XrVrVtX33zzjZYsWaLTp09Lkr799luNHz/epQUCAAAURL7CznPPPaeJEycqISFBHh4ejvbWrVtr06ZNLisOAACgoPIVdr777js98MADudrLlCmjY8eOFbgoAAAAV8lX2AkICNDhw4dzte/YsUPly5cvcFEAAACukq+w07NnT40ePVopKSmy2WzKzs7W119/rZEjR6p3794uLfC3337TY489pqCgIHl7e6tBgwbatm2bY7oxRjExMSpXrpy8vLzUqlUrLpIGAAAO+Qo7kyZNUoUKFVS+fHmdPn1atWrV0j333KPmzZvrhRdecFlxx48fV4sWLeTu7q7ly5drz549evXVVxUQEODoM2XKFE2dOlUzZ85UYmKiQkJCFBUVpVOnTrmsDgAAcPMqmZ+Z3N3d9d577+nFF1/Ujh07lJ2drYYNG6pq1aouLe6ll15SWFiY5s+f72irWLGi49/GGMXFxWns2LHq3r27JGnBggUKDg7WwoULNWDAAJfWAwAAbj4Fuqlg5cqV9eCDD6pHjx4uDzqS9Omnn6px48Z66KGHVLZsWTVs2FBvvPGGY/qBAweUkpKi9u3bO9rsdrsiIyO1ceNGl9cDAABuPvk6sjN8+PA82202mzw9PVWlShV17dpVgYGBBSrul19+0ezZszV8+HA9//zz2rJli/72t7/Jbrerd+/eSklJkSQFBwc7zRccHKxDhw5ddrmZmZnKzMx0vD958mSB6gQAAMVXvsLOjh07tH37dmVlZal69eoyxuinn36Sm5ubatSooVmzZmnEiBHasGGDatWqle/isrOz1bhxY02ePFmS1LBhQ+3evVuzZ892uhDaZrM5zWeMydV2qdjYWE2YMCHfdQEAgJtHvk5jde3aVe3atdPvv/+ubdu2afv27frtt98UFRWlRx99VL/99pvuueceDRs2rEDFhYaG5gpLNWvWVFJSkiQpJCREkhxHeHKkpqbmOtpzqTFjxigtLc3xSk5OLlCdAACg+MpX2Hn55Zf1j3/8Q/7+/o42f39/xcTEaMqUKfL29ta4ceOchojnR4sWLfTDDz84tf34448KDw+XJEVERCgkJEQJCQmO6efOndP69evVvHnzyy7XbrfL39/f6QUAAKwpX2EnLS3N8fDPSx09etRx/UtAQIDOnTtXoOKGDRumzZs3a/Lkyfr555+1cOFCzZ07V4MGDZJ08fTV0KFDNXnyZC1dulTff/+9+vbtK29vb/Xs2bNA6wYAANaQr2t2unbtqieffFKvvvqq7rzzTtlsNm3ZskUjR45Ut27dJElbtmxRtWrVClTcnXfeqaVLl2rMmDF68cUXFRERobi4OPXq1cvRZ9SoUUpPT1d0dLSOHz+upk2bauXKlfLz8yvQugEAgDXkK+y8/vrrGjZsmB555BFduHDh4oJKllSfPn00bdo0SVKNGjX05ptvFrjAzp07q3PnzpedbrPZFBMTo5iYmAKvCwAAWE++wo6vr6/eeOMNTZs2Tb/88ouMMapcubJ8fX0dfRo0aOCqGgEAAPItX2Enh6+vr+rVq+eqWgAAAFwu32EnMTFRH374oZKSknJdiLxkyZICFwYAAOAK+RqNtWjRIrVo0UJ79uzR0qVLdf78ee3Zs0dr1qxRqVKlXF0jAABAvuUr7EyePFnTpk3T559/Lg8PD02fPl179+5Vjx49VKFCBVfXCAAAkG/5Cjv79+9Xp06dJF28Qd+ZM2dks9k0bNgwzZ0716UFAgAAFES+wk5gYKBOnTolSSpfvry+//57SdKJEyd09uxZ11UHAABQQPm6QLlly5ZKSEhQ3bp11aNHDw0ZMkRr1qxRQkKC2rZt6+oaAQAA8i1fYWfmzJnKyMiQdPGhmu7u7tqwYYO6d++uv//97y4tEAAAoCDyFXYCAwMd/y5RooRGjRqlUaNGuawoAAAAV8nXNTtubm55Pgj02LFjcnNzK3BRAAAArpKvsGOMybM9MzNTHh4eBSoIAADAla7rNNa//vUvSRcfvvnmm286PQsrKytLX331lWrUqOHaCgEAAArgusJOzhPNjTGaM2eO0ykrDw8PVaxYUXPmzHFthQAAAAVwXWHnwIEDkqTWrVtryZIluu22225IUQAAAK6Sr9FYa9eudXUdAAAAN0S+wk5WVpbi4+O1evVqpaamKjs722n6mjVrXFIcAABAQeUr7AwZMkTx8fHq1KmT6tSpI5vN5uq6AAAAXCJfYWfRokX64IMPdN9997m6HgAAAJfK1312PDw8VKVKFVfXAgAA4HL5CjsjRozQ9OnTL3tzQQAAgOIiX6exNmzYoLVr12r58uWqXbu23N3dnaYvWbLEJcUBAAAUVL7CTkBAgB544AFX1wIAAOBy+Qo78+fPd3UdAAAAN0S+rtmRpAsXLmjVqlV6/fXXderUKUnS77//rtOnT7usOAAAgILK15GdQ4cO6d5771VSUpIyMzMVFRUlPz8/TZkyRRkZGTwfCwAAFBv5OrIzZMgQNW7cWMePH5eXl5ej/YEHHtDq1atdVhwAAEBB5Xs01tdffy0PDw+n9vDwcP32228uKQwAAMAV8nVkJzs7W1lZWbnaf/31V/n5+RW4KAAAAFfJV9iJiopSXFyc473NZtPp06c1fvx4HiEBAACKlXydxpo2bZpat26tWrVqKSMjQz179tRPP/2k0qVL6/3333d1jQAAAPmWr7BTrlw57dy5U4sWLdK2bduUnZ2tfv36qVevXk4XLAMAABS1fIUdSfLy8tITTzyhJ554wpX1AAAAuFS+rtmJjY3VvHnzcrXPmzdPL730UoGLAgAAcJV8hZ3XX39dNWrUyNVeu3ZtbigIAACKlXyFnZSUFIWGhuZqL1OmjA4fPlzgogAAAFwlX2EnLCxMX3/9da72r7/+WuXKlStwUQAAAK6SrwuU+/fvr6FDh+r8+fNq06aNJGn16tUaNWqURowY4dICAQAACiJfYWfUqFH6448/FB0drXPnzkmSPD09NXr0aI0ZM8alBQIAABTEdYedrKwsbdiwQaNHj9bf//537d27V15eXqpatarsdvuNqBEAACDfrjvsuLm5qUOHDtq7d68iIiJ055133oi6AAAAXCJfFyjXrVtXv/zyi6trAQAAcLl8hZ1JkyZp5MiR+vzzz3X48GGdPHnS6QUAAFBc5OsC5XvvvVeSdP/998tmsznajTGy2WzKyspyTXUAAAAFlK+ws3btWlfXAQAAcEPkK+xERka6ug4AAIAbIl/X7EjSf/7zHz322GNq3ry5fvvtN0nSO++8ow0bNrisOAAAgILKV9j5+OOP1aFDB3l5eWn79u3KzMyUJJ06dUqTJ092aYEAAAAFka+wM3HiRM2ZM0dvvPGG3N3dHe3NmzfX9u3bXVYcAABAQeUr7Pzwww+65557crX7+/vrxIkTBa0JAADAZfIVdkJDQ/Xzzz/nat+wYYMqVapU4KIAAABcJV9hZ8CAARoyZIi++eYb2Ww2/f7773rvvfc0cuRIRUdHu7pGAACAfMv3U89Pnjyp1q1bKyMjQ/fcc4/sdrtGjhypwYMHu7pGAACAfLuusHP27Fk9++yzWrZsmc6fP68uXbpoxIgRkqRatWrJ19f3hhQJAACQX9cVdsaPH6/4+Hj16tVLXl5eWrhwobKzs/Xhhx/eqPoAAAAK5LrCzpIlS/TWW2/pkUcekST16tVLLVq0UFZWltzc3G5IgQAAAAVxXRcoJycnq2XLlo73TZo0UcmSJfX777+7vDAAAABXuK6wk5WVJQ8PD6e2kiVL6sKFCy4tCgAAwFWu6zSWMUZ9+/aV3W53tGVkZGjgwIHy8fFxtC1ZssR1FQIAABTAdYWdPn365Gp77LHHXFYMAACAq11X2Jk/f/6NquOaxMbG6vnnn9eQIUMUFxcn6eLRpgkTJmju3Lk6fvy4mjZtqtdee021a9cu0loBAEDxkK87KBeFxMREzZ07V/Xq1XNqnzJliqZOnaqZM2cqMTFRISEhioqK0qlTp4qoUgAAUJzk6w7Khe306dPq1auX3njjDU2cONHRboxRXFycxo4dq+7du0uSFixYoODgYC1cuFADBgwoqpJRzFXMWFjUJbjEwaIuAABuAjfFkZ1BgwapU6dOateunVP7gQMHlJKSovbt2zva7Ha7IiMjtXHjxssuLzMzUydPnnR6AQAAayr2R3YWLVqkbdu2aevWrbmmpaSkSJKCg4Od2oODg3Xo0KHLLjM2NlYTJkxwbaEAAKBYKtZHdpKTkzVkyBC999578vT0vGw/m83m9N4Yk6vtUmPGjFFaWprjlZyc7LKaAQBA8VKsj+xs27ZNqampatSokaMtKytLX331lWbOnKkffvhB0sUjPKGhoY4+qampuY72XMputzvdKwgAAFhXsT6y07ZtW3333XfauXOn49W4cWP16tVLO3fuVKVKlRQSEqKEhATHPOfOndP69evVvHnzIqwcAAAUF8X6yI6fn5/q1Knj1Obj46OgoCBH+9ChQzV58mRVrVpVVatW1eTJk+Xt7a2ePXsWRckAAKCYKdZh51qMGjVK6enpio6OdtxUcOXKlfLz8yvq0gAAQDFw04WddevWOb232WyKiYlRTExMkdQDAACKt2J9zQ4AAEBBEXYAAIClEXYAAICl3XTX7NzMrPA8poNFXQAAANeJIzsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSCDsAAMDSShZ1AQBubRUzFhZ1CS5xsKgLAHBZHNkBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAACWVrKoCwAAALlVzFhY1CUU2MGiLuD/cWQHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGmEHAABYGndQBgBIssYde6Xic9deFB8c2QEAAJZG2AEAAJZG2AEAAJZWrMNObGys7rzzTvn5+als2bLq1q2bfvjhB6c+xhjFxMSoXLly8vLyUqtWrbR79+4iqhgAABQ3xTrsrF+/XoMGDdLmzZuVkJCgCxcuqH379jpz5oyjz5QpUzR16lTNnDlTiYmJCgkJUVRUlE6dOlWElQMAgOKiWI/GWrFihdP7+fPnq2zZstq2bZvuueceGWMUFxensWPHqnv37pKkBQsWKDg4WAsXLtSAAQOKomwAAFCMFOsjO3+WlpYmSQoMDJQkHThwQCkpKWrfvr2jj91uV2RkpDZu3HjZ5WRmZurkyZNOLwAAYE03Tdgxxmj48OG6++67VadOHUlSSkqKJCk4ONipb3BwsGNaXmJjY1WqVCnHKyws7MYVDgAAitRNE3YGDx6sb7/9Vu+//36uaTabzem9MSZX26XGjBmjtLQ0xys5Odnl9QIAgOKhWF+zk+OZZ57Rp59+qq+++kq33367oz0kJETSxSM8oaGhjvbU1NRcR3suZbfbZbfbb1zBAACg2CjWR3aMMRo8eLCWLFmiNWvWKCIiwml6RESEQkJClJCQ4Gg7d+6c1q9fr+bNmxd2uQAAoBgq1kd2Bg0apIULF+qTTz6Rn5+f4zqcUqVKycvLSzabTUOHDtXkyZNVtWpVVa1aVZMnT5a3t7d69uxZxNUDAIDioFiHndmzZ0uSWrVq5dQ+f/589e3bV5I0atQopaenKzo6WsePH1fTpk21cuVK+fn5FXK1AACgOCrWYccYc9U+NptNMTExiomJufEFAQCAm06xvmYHAACgoAg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0gg7AADA0iwTdmbNmqWIiAh5enqqUaNG+s9//lPUJQEAgGLAEmFn8eLFGjp0qMaOHasdO3aoZcuW6tixo5KSkoq6NAAAUMQsEXamTp2qfv36qX///qpZs6bi4uIUFham2bNnF3VpAACgiN30YefcuXPatm2b2rdv79Tevn17bdy4sYiqAgAAxUXJoi6goP773/8qKytLwcHBTu3BwcFKSUnJc57MzExlZmY63qelpUmSTp48eeMKlZSdefaGLr8w3OjPqLBYYV9I1tgf7Ivig31RvFhhf9zofZGzfGPMFfvd9GEnh81mc3pvjMnVliM2NlYTJkzI1R4WFnZDarOSUnFFXQEuxf4oPtgXxQf7ovgorH1x6tQplSpV6rLTb/qwU7p0abm5ueU6ipOamprraE+OMWPGaPjw4Y732dnZ+uOPPxQUFHTZgHQzOHnypMLCwpScnCx/f/+iLueWxr4oPtgXxQf7oviwyr4wxujUqVMqV67cFfvd9GHHw8NDjRo1UkJCgh544AFHe0JCgrp27ZrnPHa7XXa73aktICDgRpZZqPz9/W/qH14rYV8UH+yL4oN9UXxYYV9c6YhOjps+7EjS8OHD9fjjj6tx48Zq1qyZ5s6dq6SkJA0cOLCoSwMAAEXMEmHn4Ycf1rFjx/Tiiy/q8OHDqlOnjv79738rPDy8qEsDAABFzBJhR5Kio6MVHR1d1GUUKbvdrvHjx+c6RYfCx74oPtgXxQf7ovi41faFzVxtvBYAAMBN7Ka/qSAAAMCVEHYAAIClEXYAAIClEXYAAIClEXYAALgF3Urjkywz9PxW8+uvv2r27NnauHGjUlJSZLPZFBwcrObNm2vgwIE85wsAcEV2u127du1SzZo1i7qUG46h5zehDRs2qGPHjgoLC1P79u0VHBwsY4xSU1OVkJCg5ORkLV++XC1atCjqUiEpOTlZ48eP17x584q6lFtCenq6tm3bpsDAQNWqVctpWkZGhj744AP17t27iKq7tezdu1ebN29Ws2bNVKNGDe3bt0/Tp09XZmamHnvsMbVp06aoS7wlXPosyEtNnz5djz32mIKCgiRJU6dOLcyyChVh5yZ055136u6779a0adPynD5s2DBt2LBBiYmJhVwZ8rJr1y7dcccdysrKKupSLO/HH39U+/btlZSUJJvNppYtW+r9999XaGioJOnIkSMqV64c+6IQrFixQl27dpWvr6/Onj2rpUuXqnfv3qpfv76MMVq/fr2+/PJLAk8hKFGihOrXr5/rGZDr169X48aN5ePjI5vNpjVr1hRNgYWAsHMT8vLy0s6dO1W9evU8p+/bt08NGzZUenp6IVd2a/r000+vOP2XX37RiBEj+AVbCB544AFduHBB8+fP14kTJzR8+HB9//33WrdunSpUqEDYKUTNmzdXmzZtNHHiRC1atEjR0dF6+umnNWnSJEnS2LFjlZiYqJUrVxZxpdYXGxurN954Q2+++aZTuHR3d9euXbtyHQG1JIObTkREhJk3b95lp8+bN89EREQUYkW3NpvNZkqUKGFsNttlXyVKlCjqMm8JZcuWNd9++61TW3R0tKlQoYLZv3+/SUlJYV8UEn9/f/PTTz8ZY4zJysoyJUuWNNu2bXNM/+6770xwcHBRlXfL2bJli6lWrZoZMWKEOXfunDHGmJIlS5rdu3cXcWWFgwuUb0IjR47UwIEDtW3bNkVFRSk4OFg2m00pKSlKSEjQm2++qbi4uKIu85YRGhqq1157Td26dctz+s6dO9WoUaPCLeoWlZ6erpIlnb/WXnvtNZUoUUKRkZFauHBhEVV2aytRooQ8PT2dTqP4+fkpLS2t6Iq6xdx5553atm2bBg0apEaNGum9996TzWYr6rIKDWHnJhQdHa2goCBNmzZNr7/+uuOQvJubmxo1aqS3335bPXr0KOIqbx2NGjXS9u3bLxt2bDbbLTXEsyjVqFFDW7duzTW6ZMaMGTLG6P777y+iym49FStW1M8//6wqVapIkjZt2qQKFSo4picnJzuupULh8PX11YIFC7Ro0SJFRUXdUqdzuWbnJnf+/Hn997//lSSVLl1a7u7uRVzRrec///mPzpw5o3vvvTfP6WfOnNHWrVsVGRlZyJXdemJjY/Wf//xH//73v/OcHh0drTlz5ig7O7uQK7v1zJkzR2FhYerUqVOe08eOHasjR47ozTffLOTKIF28fcm2bdvUrl07+fj4FHU5NxxhBwAAWBp3UAYAAJZG2AEAAJZG2AEAAJZG2AFw06lYsSK3VwBwzQg7AFCEYmJi1KBBg6IuA7A0wg4AALA0wg6AfDPGaMqUKapUqZK8vLxUv359ffTRR5KkdevWyWaz6csvv1TDhg3l5eWlNm3aKDU1VcuXL1fNmjXl7++vRx99VGfPnnUss1WrVho8eLAGDx6sgIAABQUF6YUXXrjijRmTkpIcD5309/dXjx49dOTIEUnSwYMHVaJECW3dutVpnhkzZig8PFzGmHzXeqXtv/QzWL16tRo3bixvb281b95cP/zwgyQpPj5eEyZM0K5du2Sz2WSz2RQfH1/g/QLgT4rqORUAbn7PP/+8qVGjhlmxYoXZv3+/mT9/vrHb7WbdunVm7dq1RpK56667zIYNG8z27dtNlSpVTGRkpGnfvr3Zvn27+eqrr0xQUJD55z//6VhmZGSk8fX1NUOGDDH79u0z7777rvH29jZz58519AkPDzfTpk0zxhiTnZ1tGjZsaO6++26zdetWs3nzZnPHHXeYyMhIR/+oqCgTHR3tVHvDhg3NuHHjjDEm37VeafsvXW7Tpk3NunXrzO7du03Lli1N8+bNjTHGnD171owYMcLUrl3bHD582Bw+fNicPXvWpfsIgDGEHQD5cvr0aePp6Wk2btzo1N6vXz/z6KOPOn7Rr1q1yjEtNjbWSDL79+93tA0YMMB06NDB8T4yMtLUrFnTZGdnO9pGjx5tatas6Xh/adhZuXKlcXNzM0lJSY7pu3fvNpLMli1bjDHGLF682Nx2220mIyPDGGPMzp07jc1mMwcOHDDGmHzVerXtv9xyv/jiCyPJpKenG2OMGT9+vKlfv36enzEA1+A0FoB82bNnjzIyMhQVFSVfX1/H6+2339b+/fsd/erVq+f4d3BwsLy9vVWpUiWnttTUVKdl33XXXU4PKWzWrJl++umnPJ/ls3fvXoWFhSksLMzRVqtWLQUEBGjv3r2SpG7duqlkyZJaunSpJGnevHlq3bq1Klas6LSs66n1Wrf/z8vNeR7Un7cZwI3Dg0AB5EvO86W++OILlS9f3mma3W53/MK/9HltNpst1/PbbDZbgZ5VZYzJ8+nNl7Z7eHjo8ccf1/z589W9e3ctXLgwz6Hr11Pr1bb/Ssu9dH4ANx5hB0C+1KpVS3a7XUlJSXk+5PTPRzeux+bNm3O9r1q1qtzc3PKsIykpScnJyY6jO3v27FFaWprT08/79++vOnXqaNasWTp//ry6d++e7/py1nul7b9WHh4et9TTp4GiQNgBkC9+fn4aOXKkhg0bpuzsbN199906efKkNm7cKF9fX4WHh+d72cnJyRo+fLgGDBig7du3a8aMGXr11Vfz7NuuXTvVq1dPvXr1UlxcnC5cuKDo6GhFRkaqcePGjn41a9bUXXfdpdGjR+vJJ5+Ul5dXvuuTrr79ffr0uablVKxYUQcOHNDOnTt1++23y8/PL9eRIQAFQ9gBkG//+Mc/VLZsWcXGxuqXX35RQECA7rjjDj3//PMFOk3Tu3dvpaenq0mTJnJzc9Mzzzyjp556Ks++NptNy5Yt0zPPPKN77rlHJUqU0L333qsZM2bk6tuvXz9t3LhRTz75ZL5ru9SVtv9a/eUvf9GSJUvUunVrnThxQvPnz1ffvn1dUh+Ai2zGXOHmFQBQyFq1aqUGDRrckMdBTJo0SYsWLdJ3333n8mUDKL4YjQXA8k6fPq3ExETNmDFDf/vb34q6HACFjLADwPIGDx6su+++W5GRkS47hQXg5sFpLAAAYGkc2QEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJb2f5/bEeA9V4ITAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 定义要分析的变量列表\n",
    "variables = ['checking_status', 'credit_history', 'savings_status', 'employment']\n",
    "\n",
    "# 循环生成图表\n",
    "for var in variables:\n",
    "    # 使用交叉表计算交叉百分比\n",
    "    cross_tab = pd.crosstab(data[var], data['class'], normalize='index') * 100\n",
    "\n",
    "    # 绘制交叉百分比柱状图\n",
    "    cross_tab.plot(kind='bar', stacked=True)\n",
    "    plt.xlabel(var)\n",
    "    plt.ylabel('Percentage')\n",
    "    plt.title(f'Cross Effects between {var} and class')\n",
    "    plt.legend(title='class')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图1 checking_status表示申请人的支票账户状态，它有4个类别：无账户、余额小于0欧元、余额低于200欧元，余额高于200欧元。从图中可以看出，无账户和小于0欧元的申请人有较高的坏信用风险，而大于200欧元的申请人有较低的坏信用风险。这说明支票账户状态与信用风险呈负相关关系。\n",
    "\n",
    "图2 credit_history表示申请人的信用历史，它有五个类别：无信用/所有信用已付清、所有信用已付清/延期付款、现有信用/延期付款、严重违约和其他。从图中可以看出，严重违约和现有信用/延期付款的申请人有较高的坏信用风险，而无信用/所有信用已付清和所有信用已付清/延期付款的申请人有较低的坏信用风险。这说明信用历史与信用风险呈正相关关系。\n",
    "\n",
    "图3 savings_status表示申请人的储蓄账户状态，它有五个类别：无储蓄、小于100欧元、100-500欧元、500-1000欧元和大于1000欧元。从图中可以看出，无储蓄和小于100欧元的申请人有较高的坏信用风险，而大于1000欧元的申请人有较低的坏信用风险。这说明储蓄账户状态与信用风险呈负相关关系。\n",
    "\n",
    "图4 employment表示申请人的就业状况，它有五个类别：失业、在职分别小于1年、1-4年、4-7年和大于7年。从图中可以看出，失业和在职小于1年的申请人有较高的坏信用风险，而大于7年的申请人有较低的坏信用风险。这说明就业状况与信用风险呈负相关关系。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 三、 箱线图\n",
    "\n",
    "下图反映了不同目标变量类别（class：good/bad）对两个不同特征（duration，credit_amount）的分布情况的影响。duration表示申请人的贷款期限，单位是月。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制箱线图观察重要数值型特征与分类目标之间的关系\n",
    "variables = ['duration', 'credit_amount']\n",
    "\n",
    "# 循环生成图表\n",
    "for var in variables:\n",
    "    sns.boxplot(x='class', y=var, data=data)\n",
    "    plt.xlabel(\"class\")\n",
    "    plt.ylabel(var)\n",
    "    plt.title(f'Interaction between {var} and class')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图1中可以看出，坏信用风险的申请人（class=1）的贷款期限分布比好信用风险的申请人（class=0）更广泛，也更偏向于较长的期限。这说明贷款期限与信用风险呈正相关关系。\n",
    "\n",
    "图2中credit_amount表示申请人的贷款金额，单位是马克。从图4-12中可以看出，坏信用风险的申请人（class=1）的贷款金额分布比好信用风险的申请人（class=0）更广泛，也更偏向于较高的金额。这说明贷款金额与信用风险呈正相关关系。\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 五、 总结"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （一）算法比较\n",
    "\n",
    "**GradientBoostingClassifier**和**RandomForestClassifier**都是基于决策树的集成学习方法，实验过程中发现它们的区别如下：\n",
    "\n",
    "核心思想\n",
    "- GradientBoostingClassifier是一种**提升**方法，它通过迭代地拟合残差的负梯度来优化任意可微分的损失函数。\n",
    "- RandomForestClassifier是一种**随机森林**方法，它通过对多个独立的决策树进行平均或投票来降低方差。\n",
    "- GradientBoostingClassifier是一种**串行**的算法，每一棵树都依赖于前一棵树的结果。\n",
    "- RandomForestClassifier是一种**并行**的算法，每一棵树都可以独立地训练。\n",
    "\n",
    "超参数\n",
    "- GradientBoostingClassifier主要通过调整学习率、子采样比例、正则化项等参数来控制拟合过程。\n",
    "- RandomForestClassifier主要通过调整树的数量、最大深度、最小叶子节点数等参数来控制拟合过程。\n",
    "\n",
    "速度：\n",
    "- GradientBoostingClassifier通常需要更多的时间和内存来训练和预测，因为它需要多次迭代和梯度计算。\n",
    "- RandomForestClassifier通常比较快速和节省内存，因为它只需要一次训练和预测。\n",
    "\n",
    "对样本扰动敏感度：\n",
    "- RandomForestClassifier对类别不平衡的数据更敏感，出现类别不平衡需要进行处理后再使用随机森林。"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### （二）算法改进方向\n",
    "\n",
    "- 特征筛选\n",
    "\n",
    "根据**特征重要性排序图**，可以看到有一些特征对分类的贡献非常微弱，可以考虑再次进行特征筛选，再做分类，可以加快模型拟合的速度。\n",
    "\n",
    "- 超参数调优\n",
    "\n",
    "除了网格搜索，还可以使用**贝叶斯优化**的方法来进行调优。\n",
    "\n",
    "- 类别不平衡\n",
    "\n",
    "除了正则化的方法，还可以通过**重采样**的方式进行类别平衡。"
   ]
  }
 ],
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