Information Gain is a widely used concept in machine learning and decision trees. It helps measure how effective a feature is in classifying or predicting data and is commonly associated with the ID3 algorithm for constructing decision trees. The basic idea behind Information Gain is to determine how well a particular feature separates the data into different classes. This helps decide which feature should be used to split the data at a given node in a decision tree. Therefore, the feature with the highest Information Gain is chosen as the splitting criterion. Here’s a step-by-step explanation of how Information Gain is calculated:

1. Entropy (H): Entropy measures the impurity or disorder in a set of data. In the context of decision trees, it represents the uncertainty associated with classifying an instance in a given dataset.

2. Information Gain (IG): Information Gain is the reduction in entropy, or the amount of uncertainty removed from the dataset when a dataset is split by a specific feature.

3. Selection of Feature: The feature with the highest Information Gain is chosen as the splitting criterion at each node of the decision tree.