What is a decision tree in machine learning?

A decision tree in machine learning is a supervised learning algorithm used for both classification and regression tasks. It is a flowchart-like structure where each internal node represents a decision based on a feature attribute, each branch represents the outcome of the decision, and each leaf node represents the final prediction or outcome.

In classification tasks, decision trees recursively split the dataset into subsets based on the values of input features, aiming to minimize impurity or maximize information gain at each step. Popular impurity measures include Gini impurity and entropy. The decision tree continues to split the dataset until a stopping criterion is met, such as reaching a maximum depth, achieving a minimum number of samples per leaf, or when further splitting does not improve the model’s performance.

In regression tasks, decision trees predict the value of a target variable by recursively partitioning the input space into regions and predicting the average value of the target variable within each region. Similar to classification, decision trees aim to minimize variance or maximize information gain at each split.

Decision trees are easy to understand, interpret, and visualize, making them particularly useful for exploratory data analysis and model explanation. However, they are prone to overfitting, especially when the tree grows too deep or when the dataset is noisy. To address this, techniques such as pruning, setting maximum depth, and using ensemble methods like random forests and gradient boosting are commonly employed. Overall, decision trees are versatile and powerful algorithms widely used in various machine learning applications.