Gini Change

Learn how the CART classification tree algorithm chooses optimal splits during tree building.

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Minimizing Gini impurity

Gini impurity is the math behind how the CART classification tree algorithm quantifies the impurity for a collection of labels. The overall objective of the algorithm is to minimize impurity across all the training data.

With an intuitive understanding of how the algorithm splits data at each stage of tree building to minimize impurity, it’s time to learn the math of how the CART algorithm builds entire trees with the lowest impurity possible via the Gini change calculation.

Gini change

The Gini change calculation is defined as follows:

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