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Decision tree learning is a machine learning technique used for both classification and regression tasks. It involves constructing a tree-like structure where internal nodes represent feature attributes, branches represent decision rules, and leaf nodes represent the outcome or prediction. The algorithm recursively splits the data based on feature attributes to create increasingly pure subsets until a stopping criterion is met. Decision trees are intuitive, easy to interpret, and capable of handling both numerical and categorical data, making them widely used in various domains for their simplicity and effectiveness.
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10 lessons
Language: English
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