Answer / Rohit Vaish

Gini Impurity and Entropy are both criteria used in a decision tree for splitting nodes. They measure the impurity (disorder) or randomness of a node's data with respect to the target variable. Gini Impurity calculates the probability of incorrectly labeling a randomly chosen element from the node, while Entropy measures the amount of information required to predict the target variable at the node.

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