Decision Tree in Machine Learning

A Decision Tree is a popular machine learning algorithm used for both classification and regression tasks. It builds a tree-like model of decisions based on features in the training data. The tree splits the data into subsets by asking questions about the features at each internal node. The goal is to maximize information gain or minimize impurity (e.g., Gini impurity or entropy) at each split, leading to distinct leaf nodes representing different classes or regression values. Decision Trees are interpretable and can handle both numerical and categorical data. They can be prone to overfitting but are often used standalone or as building blocks in ensemble methods like Random Forests.