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    Machine Learning Tutorial 0/98 lessons ~6 min read Lesson 38

    Gaussian Naive Bayes

    What is Gaussian Naive Bayes?

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

    Introduction

    What is Gaussian Naive Bayes? Continuous features modeled with Gaussian distributions per class. Machine learning systems learn patterns from data instead of hard-coded rules.

    Understanding the topic

    How Gaussian Naive Bayes works:

    • Continuous features modeled with Gaussian distributions per class.
    • Prepare or explore data as needed.
    • Train or apply the model/technique.
    • Evaluate results and iterate.
    TermDescription
    Gaussian Naive BayesContinuous features modeled with Gaussian distributions per class
    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 Gaussian Naive Bayes.
    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

    1Gaussian Naive Bayes workflow
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    Understand

    Learn when and why to use Gaussian Naive Bayes.

    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

    Gaussian Naive Bayes is a core machine learning topic. Continuous features modeled with Gaussian distributions per class

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