How does a decision tree algorithm work?

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A decision tree algorithm is a supervised machine learning method used for classification and regression tasks. It works by splitting data into branches based on feature values, forming a tree-like structure where each internal node represents a condition, each branch represents an outcome, and each leaf node represents a final prediction.

How it works step by step:

1. Root Node Selection

   • The algorithm starts at the root with the entire dataset.

   • It decides which feature and threshold best splits the data into groups that are as “pure” as possible (i.e., containing mostly one class).

2. Splitting Criteria

   • Common metrics:

     • Gini Impurity → Measures how mixed the classes are.

     • Entropy/Information Gain → Measures the reduction in uncertainty after the split.

     • Variance Reduction → Used for regression trees.

3. Recursive Partitioning

   • The dataset is split into subsets, and the process repeats recursively for each child node.

   • This continues until a stopping condition is met (e.g., maximum depth, minimum samples per node, or perfectly pure leaves).

4. Leaf Nodes

   • Each terminal node (leaf) represents a final prediction:

     • For classification → the majority class in that node.

     • For regression → the average value of samples in that node.

Advantages

• Easy to interpret and visualize.

• Handles both numerical and categorical data.

• Requires little preprocessing.

Limitations

• Can overfit if not pruned (tree grows too deep).

• Sensitive to small data changes.

• Often outperformed by ensemble methods (e.g., Random Forest, XGBoost).

👉 In short: A decision tree asks a sequence of yes/no questions based on features until it reaches a decision at the leaf node.

Read More :

What is cross-validation, and why do we use it?

What are precision, recall, and F1-score?

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