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    Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 31

    Decision Tree Classification

    What is Decision Tree Classification?

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    Focus
    7 guided sections
    Practice signal
    Examples included
    Career prep
    Foundation builder

    Introduction

    What is Decision Tree Classification? Predict class labels via majority vote in leaves. Machine learning systems learn patterns from data instead of hard-coded rules.

    Understanding the topic

    How Decision Tree Classification works:

    • Predict class labels via majority vote in leaves.
    • Prepare or explore data as needed.
    • Train or apply the model/technique.
    • Evaluate results and iterate.
    TermDescription
    Decision Tree ClassificationPredict class labels via majority vote in leaves
    Training dataExamples used to learn patterns.
    FeaturesInput variables (columns) fed to the model.
    Target / labelWhat you predict (supervised learning).

    Step-by-step explanation

    1. Understand — Learn when and why to use Decision Tree Classification.
    2. Prepare data — Load, clean, and split datasets.
    3. Apply — Fit model or run algorithm in Python/sklearn.
    4. Evaluate — Measure accuracy, loss, or cluster quality.

    Execution workflow

    1Decision Tree Classification workflow
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    Understand

    Learn when and why to use Decision Tree Classification.

    Best practices

    • Split data into train/validation/test before tuning.
    • Scale numeric features when algorithms are distance-based.
    • Always evaluate on held-out data — not training accuracy alone.

    Common mistakes

    • Training on test data (data leakage).
    • Ignoring class imbalance in classification metrics.
    • Using accuracy alone on imbalanced datasets.

    Summary

    Decision Tree Classification is a core machine learning topic. Predict class labels via majority vote in leaves

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