Explanation of decision trees

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Decision trees are a type of supervised machine learning algorithm used for classification and regression tasks.

Decision trees are hierarchical structures where each internal node represents a feature or attribute, each branch represents a decision rule, and each leaf node represents the outcome.
They are easy to interpret and visualize, making them popular for decision-making processes.
Decision trees can handle both numerical and categorical data, and can be used for both classification and regression tasks.
Examples of decision tree algorithms include ID3, C4.5, CART, and Random Forest.
They work by recursively partitioning the data into subsets based on the most significant attribute at each node.

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