Machine Learning
Decision Trees
A supervised learning algorithm used for classification and regression tasks that models decisions and their possible consequences as a tree-like structure.
Explanation
Decision trees are non-parametric supervised learning methods 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. A tree consists of root nodes, internal nodes representing attribute tests, branches representing outcomes, and leaf nodes representing final class labels or continuous values. They are valued for their interpretability and serve as the building blocks for ensemble methods like Random Forests and Gradient Boosting.