BreakNBuild
is an R package designed to evaluate model performance with progressively sampled data. This approach is particularly useful for debugging in machine learning, as it allows you to observe the bias-variance trade-off in relation to the sample size used for training the model.
- Progressive Data Splitting: partition your dataset into training and validation subsets.
- Customizable Sample Sizes: Control the size of your training data to understand model performance under different conditions.
- Easy Integration: Built on the
rsample
package,BreakNBuild
seamlessly integrates with thetidymodels
framework.
![man/figures/schema_progressive_split.svg]
To install the latest version from GitHub, use:
# install.packages("devtools")
devtools::install_github("https://github.com/focardozom/BreakNBuild")
Here's a quick example to get you started:
library(BreakNBuild)
splits <- progressive_splits(data, validation_size = 0.2, start_size = 10)