decisiontree

A decision tree is a predictive model used for classification and regression that represents decisions and their possible consequences as a tree-like graph of nodes. Each internal node corresponds to a test on a feature, each branch represents the outcome of the test, and each leaf node holds a predicted value or class. Decision trees are widely used for their simplicity, interpretability, and ability to handle both numerical and categorical data.

Construction of a decision tree involves recursively partitioning the data set into subsets that are increasingly homogeneous with respect to the target variable. Splits are chosen to optimize a criterion such as information gain, Gini impurity, or entropy, depending on the algorithm. Popular algorithms include CART (which supports both classification and regression), ID3, C4.5, and C5.0. The model performs predictions by traversing the tree from the root to a leaf based on feature values. Pruning and stopping rules are commonly applied to reduce overfitting by limiting tree depth or removing branches that do not improve performance on validation data.

Decision trees offer several advantages, including ease of interpretation, minimal data preparation, and the ability to handle mixed data types. However, they can be prone to overfitting, be unstable with small data changes, and exhibit biased splits toward features with many levels. They also create axis-aligned decision boundaries, which may be inefficient for complex patterns.

Many models build on decision trees, such as random forests and gradient boosting, which combine multiple trees to improve accuracy and robustness while sacrificing some interpretability. Decision trees remain a practical tool for quick insight, feature importance assessment, and transparent decision-making in various domains.