Objectives

The objectives of this section are:

to explore training and testing errors and how they are influenced by the complexity of the tree.
to present the balancing act needed for an optimal decision tree.
to explain the concept of overfitting and underfitting.
to introduce you to the measure estimation of generalization errors.
to explore how overfitting is handle in decision tree induction algorithms.

Outcomes

By the time you have completed this section you will be able to:

to define training errors, testing errors, overfitting & underfitting.
to explain the balancing act needed to avoid either extreme.
to give a brief synopsis of the measures used to estimate generalization errors.
to explain how overfitting is handle in decision tree induction algorithms.