added under/overfitting to lobjs in reg1, cls2

Author: trevorcampbell

source/classification2.Rmd

@@ -60,6 +60,7 @@ By the end of the chapter, readers will be able to do the following:
 - Evaluate classification accuracy, precision, and recall in R using a test set, a single validation set, and cross-validation.
 - Produce a confusion matrix in R.
 - Choose the number of neighbors in a K-nearest neighbors classifier by maximizing estimated cross-validation accuracy.
+- Describe underfitting and overfitting, and relate it to the number of neighbors in K-nearest neighbors classification.
 - Describe the advantages and disadvantages of the K-nearest neighbors classification algorithm.
 
 ## Evaluating performance

source/regression1.Rmd

@@ -70,6 +70,7 @@ By the end of the chapter, readers will be able to do the following:
 * Evaluate K-NN regression prediction quality in R using the root mean squared prediction error (RMSPE).
 * Estimate the RMSPE in R using cross-validation or a test set.
 * Choose the number of neighbors in K-nearest neighbors regression by minimizing estimated cross-validation RMSPE.
+- Describe underfitting and overfitting, and relate it to the number of neighbors in K-nearest neighbors regression.
 * Describe the advantages and disadvantages of K-nearest neighbors regression.
 
 ## The regression problem