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source/regression1.Rmd

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

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