Node purity is a measure of how well a node in a decision tree represents a single class, with higher purity indicating that the node predominantly contains instances of one class. It is crucial for evaluating the effectiveness of splits in decision trees, impacting the model's accuracy and generalization ability.

Node Purity

Node purity

A decision tree is a flowchart-like structure used in decision-making and machine learning to model decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is particularly useful for classification and regression tasks, providing a visual and interpretable representation of decision rules derived from data features.

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