Gini [Binary Classification] [Tree]
Description
A node's Gini value represents its Gini impurity, which measures how mixed the classes are within that node. A node is considered pure (Gini = 0) when all the data points it contains belong to the same class. For instance, if a node includes only samples from one category, its impurity is zero. In contrast, nodes that contain samples from multiple categories are impure, having higher Gini values.
Formula
\[ G_i = 1 - \sum_{k=1}^{n} p_{i,k}^2 \]
- \(G_i\): Gini impurity of the \(i\)-th node
- \(p_{i,k}\): Ratio of instances of class \(k\) among the training samples in the \(i\)-th node
- \(n\): Total number of classes