Decision Tree

1.Explain Decision Tree algorithm

A decision tree is a supervised machine learning algorithm mainly used for Regression and Classification. It breaks down a data set into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The final result is a tree with decision nodes and leaf nodes. A decision tree can handle both categorical and numerical data.

2.What are Entropy and Information gain in Decision tree algorithm?

Entropy: Entropy in Decision Tree stands for homogeneity. If the data is completely homogenous, the entropy is 0, else if the data is divided (50-50%) entropy is 1.

Information Gain: Information Gain is the decrease/increase in Entropy value when the node is split.

An attribute should have the highest information gain to be selected for splitting

3.What is pruning in Decision Tree?

Pruning is a technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that provide little power to classify instances. So, when we remove sub-nodes of a decision node, this process is called pruning or opposite process of splitting.