Section outline

  • ·         Overview: Overfitting is one of the biggest hazards in machine learning. This lesson teaches what overfitting is, how it differs from underfitting, why it happens, and how to detect and prevent it. Students will learn through analogies (like studying for a test by memorization vs understanding) to grasp why a model that performs too well on training data can actually fail in the real world.

    • Micro-Topic 9.1: What is Overfitting? – Goal: Define overfitting in simple terms and illustrate its effect.

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    • Micro-Topic 9.2: Overfitting vs Underfitting – Goal: Contrast these two extremes and highlight the need for balance.

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    • Micro-Topic 9.3: Why Overfitting Happens – Goal: List the common causes and conditions that lead to overfitting.

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    • Micro-Topic 9.4: Detecting Overfitting – Goal: Learn how to tell if your model is overfitting by using validation techniques.

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    • Micro-Topic 9.5: Preventing & Fixing Overfitting – Goal: Cover strategies to avoid or reduce overfitting.

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