Impurity (Data Science) 편집하기 (부분)

==Role of Impurity in Decision Trees==
In decision trees, impurity plays a central role in determining the best splits:

1. '''Selecting Splits''': Decision trees use impurity measures to evaluate potential splits in the data, choosing splits that result in the greatest reduction in impurity. 2. '''Information Gain''': For impurity measures like entropy, the split that maximizes information gain (reduction in entropy) is selected. 3. '''Gini Gain''': When using Gini impurity, the split that maximizes the reduction in Gini impurity is chosen.

Lowering impurity at each split helps the decision tree grow branches that separate classes effectively, leading to more accurate predictions.