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Decision trees are used for classification and regression tasks from "summary" of Machine Learning by Stephen Marsland
Decision trees are versatile tools that can be used for both classification and regression tasks in machine learning. In classification, decision trees are used to divide the feature space into regions that correspond to different classes or categories. The decision tree algorithm recursively splits the data based on the values of input features, creating a tree-like structure where each internal node represents a decision based on a feature, and each leaf node represents a class label. This process continues until a stopping criterion is met, such as reaching a maximum tree depth or having all instances in a node belong to the same class. On the other hand, decision trees can also be used for regression tasks, where the goal is to predict a continuous target variable. In regression trees, the algorithm recursively partitions the feature space into regions based on the values of input features, fitting a simple model (e. g., constant value) to the target variable in each region. The prediction for a new instance is then made by traversing the tree from the ...
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