Decision trees algorithm explained

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Decision trees algorithm is a popular machine learning technique used for classification and regression tasks.

Decision trees are a type of supervised learning algorithm that is used for both classification and regression tasks.
They work by recursively splitting the data into subsets based on the most significant attribute.
Each internal node represents a test on an attribute, each branch represents the outcome of the test, and each leaf node represents the class label or the target value.
Decision trees are easy to interpret and visualize, making them popular for understanding the decision-making process of the model.
Examples of decision tree algorithms include ID3, C4.5, CART, and Random Forest.

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