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Introduction to Machine Learning
/Deterministic Models
/Tree-Based Models
/Decision Trees

Decision Trees

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Decision Trees

Decision trees are like flowcharts that make decisions by asking a series of simple questions (e.g., "Is the applicant’s income above $50,000?"). They’re easy to understand and work well with structured data, such as loan approvals, customer segmentation, or medical diagnoses. However, they can struggle with complex patterns and may overfit noisy data.

Characteristics:

  • Interpretability - Decision trees provide clear explanations for their predictions, showing exactly which features led to each decision.
  • Handle mixed data types - Trees work well with both numerical and categorical features without requiring extensive preprocessing.
  • Instability - Small changes in training data can result in completely different tree structures.

Application - Ideal for scenarios where explaining predictions is just as important as accuracy, such as credit approval or medical diagnosis.

Decision trees are intuitive models that make predictions by asking a series of questions, following a tree-like path of decisions until reaching a conclusion. They work like a flowchart, with each internal node representing a "test" on a feature (e.g., "Is income > $50,000?"), each branch representing the outcome of the test, and each leaf node representing a final prediction.

Everyday analogy: Think of how doctors diagnose patients—they ask a series of questions about symptoms, with each answer narrowing down the possible diagnoses until they reach a conclusion. Decision trees work similarly, creating a systematic approach to decision-making based on available information.

Key strengths: Decision trees are highly interpretable (you can follow the path to understand exactly why a prediction was made), handle mixed data types well, require minimal data preparation, and automatically perform feature selection. They naturally model non-linear relationships and interactions between features without requiring transformation.

Real-world applications: Credit approval systems, medical diagnosis, customer churn prediction, and automated troubleshooting guides all benefit from decision trees' transparent decision process.