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.
Handling Overfitting in Decision Tree Induction
Pre-pruning: does as the name suggests, it stops the algorithm before it comes a fully-grown tree. ADD THE SLIDE 61 as a picture
Post-pruning: in this method, the decision tree is grown to it’s maximum size and is then followed by a tree-pruning step.
