What is the default method of splitting in decision trees?

The default method of splitting in decision trees is the Gini Index. Gini Index is the measure of impurity of a particular node.

This can be changed by making changes to classifier parameters.

The default method of splitting in decision trees depends on the specific implementation or library being used. However, one common default method is the “CART” (Classification and Regression Trees) algorithm, which is often used in popular libraries like scikit-learn.

In the CART algorithm, the default method of splitting involves finding the feature and the corresponding threshold that maximally reduces the impurity of the resulting nodes. The impurity measure can be Gini impurity for classification tasks or mean squared error for regression tasks.

So, in summary, the default method of splitting in decision trees, particularly in the context of CART, involves selecting the feature and threshold that maximally reduces impurity during the tree-building process.