2. When splitting data into branches for a decision tree, what kind of feature is favored and chosen first? The feature that increases purity in the tree nodes. The feature that increases entropy in the tree nodes. The feature that splits the data equally into groups. The feature with the greatest number of categories.

Question

2. When splitting data into branches for a decision tree, what kind of feature is favored and chosen first? The feature that increases purity in the tree nodes. The feature that increases entropy in the tree nodes. The feature that splits the data equally into groups. The feature with the greatest number of categories.

Answer 1

The feature that increases purity in the tree nodes.When splitting data into branches for a decision tree, the feature that increases purity in the tree nodes is favored and chosen first. This is because increasing the purity of the tree nodes leads to a more accurate and efficient decision tree.

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