Decision trees break down decisions into a treelike structure from "summary" of Machine Learning by Ethem Alpaydin
Decision trees are a popular method for classification and regression tasks in machine learning. They break down decisions into a treelike structure, where each internal node represents a decision based on an attribute, each branch represents the outcome of that decision, and each leaf node represents a class label or a numerical value. The simplicity of decision trees lies in their intuitive representation of the decision-making process. By following a path from the root node to a leaf node, one can easily understand how a decision is made based on the values of different attributes. The clarity of decision trees stems from their hierarchical structure, which organizes decisions in a logical and easy-to-follow manner. Each decision node considers a specific attribute and its possible values, allowing the model to capture complex decision boundaries in the data. This hierarchical decomposition of the decision space enables decision trees to handle both categorical and numerical attributes, making them versatile for various types of data. The coherence of decision trees is maintained through the recursive partitioning of the data into subsets based on the attribute values. At each node, the algorithm selects the best attribute to split the data, aiming to maximize the purity of the resulting subsets. This process continues recursively until a stopping criterion is met, such as reaching a maximum tree depth or minimum number...
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