Decision Tree

/dɪˈsɪʒ.ən triː/

noun … “branching logic that learns from examples.”

Decision Tree is a supervised machine learning model that predicts outcomes by recursively splitting a dataset into subsets based on feature values. Each internal node represents a decision on a feature, each branch represents the outcome of that decision, and each leaf node represents a predicted value or class. This structure allows the model to capture nonlinear relationships, interactions between features, and hierarchical decision processes in a transparent and interpretable way.

Technically, Decision Trees use criteria such as Information Gain, Gini impurity, or variance reduction to determine the optimal feature and threshold for each split. The tree grows by repeatedly partitioning data until a stopping condition is met, such as a minimum number of samples in a leaf, a maximum depth, or no further improvement in the splitting criterion. After training, the tree can classify new instances by following the sequence of decisions from root to leaf.

Decision trees are flexible and applicable to both classification and regression tasks. In classification, they assign labels to inputs based on majority outcomes in leaves. In regression, they predict continuous values by averaging outcomes in leaves. They are often the foundational building block for ensemble methods such as Random Forest and Gradient Boosting, which combine multiple trees to improve generalization, reduce overfitting, and enhance predictive performance.

Strengths of Decision Trees include interpretability, no need for feature scaling, and the ability to handle both numerical and categorical data. Limitations include sensitivity to noisy data, tendency to overfit small datasets, and instability with slight variations in data. Pruning, setting depth limits, or using ensemble techniques can mitigate these issues, making the model robust and generalizable.

Example conceptual workflow of building a decision tree:

start with the entire dataset at the root
calculate splitting criterion for all features
select the feature that best separates the data
partition dataset into branches based on this feature
repeat recursively for each branch until stopping condition
assign leaf predictions based on majority class or average

Intuitively, a Decision Tree is like a flowchart drawn from data: every question asked splits possibilities until the answer becomes clear. It turns complex, multidimensional patterns into a path of sequential decisions, making the machine’s reasoning transparent and interpretable.