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Minor typo fix: accuracy -> RMSPE in reg1 #553

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Nov 10, 2023
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Minor typo fix: accuracy -> RMSPE in reg1 #553

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6 changes: 3 additions & 3 deletions source/regression1.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -761,10 +761,10 @@ Here we see that the smallest estimated RMSPE from cross-validation occurs when
If we want to compare this multivariable KNN regression model to the model with only a single
predictor *as part of the model tuning process* (e.g., if we are running forward selection as described
in the chapter on evaluating and tuning classification models),
then we must compare the accuracy estimated using only the training data via cross-validation.
Looking back, the estimated cross-validation accuracy for the single-predictor
then we must compare the RMSPE estimated using only the training data via cross-validation.
Looking back, the estimated cross-validation RMSPE for the single-predictor
model was `r format(round(sacr_min$mean), big.mark=",", nsmall=0, scientific = FALSE)`.
The estimated cross-validation accuracy for the multivariable model is
The estimated cross-validation RMSPE for the multivariable model is
`r format(round(sacr_multi$mean), big.mark=",", nsmall=0, scientific = FALSE)`.
Thus in this case, we did not improve the model
by a large amount by adding this additional predictor.
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