Information gain is a frequently used metric in calculating the gain during a split in tree-based methods.

First o all, the entropy of a dataset if defined as

$$S = - sum_i p_i \log p_i - sum_i (1-p_i)\log p_i,$$

where $p_i$ is the probability of a class.

The information gain is the difference between the entropy.

For example, in a decision tree algorithm, we would split a node. Before splitting, we assign a label $m$ to the node,

$$S_m = - p_m \log p_m - (1-p_m)\log p_m.$$

After the splitting, we have two groups that contributes to the entropy, group $L$ and group $R$,

$$S'_m = p_L (- p_m \log p_m - (1-p_m)\log p_m) + p_R (- p_m \log p_m - (1-p_m)\log p_m),$$

where $p_L$ and $p_R$ are the probabilities of the two groups. Suppose we have 100 samples before splitting and 29 samples in the left group and 71 samples in the right group, we have $p_L = 29/100$ and $p_R = 71/100$.

The information gain is thus

$$Gain = S_m - S'_m.$$

Planted: by ;

Supplementary:
Dynamic Backlinks to cards/machine-learning/measurement/information-gain:

L Ma (2020). 'Information Gain', Datumorphism, 01 April. Available at: https://datumorphism.leima.is/cards/machine-learning/measurement/information-gain/.