Merge branch 'main' into dollar-sign-fix

source/regression1.Rmd

@@ -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.