Machine Learning
Decision Tree
A supervised learning algorithm used for classification and regression tasks that uses a flowchart-like structure to make predictions based on data features.
Explanation
A decision tree is a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. It consists of nodes, branches, and leaves. The root node represents the entire dataset, internal nodes represent tests on attributes, branches represent the outcome of the test, and leaf nodes represent class labels or continuous values. Decision trees are highly interpretable and form the basis for more complex ensemble methods like Random Forests and Gradient Boosting Machines.