Information gain

What Is Information Gain? Information Gain, or IG for short, measures the reduction in entropy or surprise by splitting a dataset according to a given value of a random variable. A larger information gain suggests a lower entropy group or groups of samples, and hence less surprise.

1. What is information gain formula?
2. What is entropy and information gain?
3. What is information gain in decision trees?
4. Can information gain be greater than 1?

What is information gain formula?

Information Gain = Entropy before splitting - Entropy after splitting. Given a probability distribution such that. P = (p₁ , p₂ ,.......p_n ), and where (p_i) is the probability of a data point in the subset of 𝐷𝑖 of a dataset 𝐷, Therefore, Entropy is defined as the.

What is entropy and information gain?

Entropy is uncertainty/ randomness in the data, the more the randomness the higher will be the entropy. Information gain uses entropy to make decisions. If the entropy is less, information will be more. Information gain is used in decision trees and random forest to decide the best split.

What is information gain in decision trees?

Information gain is the basic criterion to decide whether a feature should be used to split a node or not. The feature with the optimal split i.e., the highest value of information gain at a node of a decision tree is used as the feature for splitting the node.

Can information gain be greater than 1?

Yes, it does have an upper bound, but not 1. The mutual information (in bits) is 1 when two parties (statistically) share one bit of information. However, they can share a arbitrary large data. In particular, if they share 2 bits, then it is 2.