added under/overfitting to learning objs in cls2, reg1

Author: trevorcampbell

source/classification2.md

@@ -39,6 +39,7 @@ By the end of the chapter, readers will be able to do the following:
 - Evaluate classification accuracy, precision, and recall in Python using a test set, a single validation set, and cross-validation.
 - Produce a confusion matrix in Python.
 - 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.
 
 +++

source/regression1.md

@@ -58,6 +58,7 @@ By the end of the chapter, readers will be able to do the following:
 * Evaluate K-NN regression prediction quality in Python using the root mean squared prediction error (RMSPE).
 * Estimate the RMSPE in Python 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.
 
 +++