An intuitive model that makes predictions based on a series of questions.
A Decision Tree is a versatile and highly intuitive supervised learning algorithm that can be used for both classification and regression tasks. It works by creating a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. The structure of the model resembles a tree, where each internal node represents a 'test' on a feature (e.g., 'Is color red?'), each branch represents the outcome of the test, and each leaf node represents a class label (in classification) or a continuous value (in regression). The algorithm builds the tree by recursively splitting the data into subsets based on the feature that provides the most 'information gain' or best separates the data. This process continues until a stopping criterion is met, such as the nodes becoming pure (containing data points of only one class) or reaching a maximum depth. One of the biggest advantages of Decision Trees is their interpretability. The tree structure can be easily visualized and understood, making it a 'white-box' model. You can follow the path from the root to a leaf to see exactly how a prediction was made. However, single decision trees are prone to overfitting, meaning they can learn the training data too well and fail to generalize to new data. This is often addressed by using them in ensembles, like Random Forests and Gradient Boosted Trees.