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DA0-002 Data Analysis Practice Question

A data scientist trains a regression model and observes high variance with low bias. Which technique is most appropriate to reduce variance?

⚠ Common exam trap

CompTIA often tests the misconception that reducing variance requires removing features or simplifying the model, but Ridge regularization is the correct technique because it penalizes coefficient magnitude without discarding predictors.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Apply Ridge regularization

Ridge regularization (L2) reduces variance by adding a penalty term proportional to the square of the coefficients, which shrinks them toward zero without eliminating them. This directly addresses high variance (overfitting) by constraining the model's complexity, while low bias indicates the model fits the training data well. The regularization parameter λ controls the trade-off between bias and variance.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Apply Ridge regularization

    Why this is correct

    Ridge adds penalty to coefficients, reducing overfitting and variance.

  • Increase polynomial features

    Why it's wrong here

    This would likely increase variance further.

  • Use a smaller training set

    Why it's wrong here

    Smaller training set typically increases variance.

  • Remove correlated features

    Why it's wrong here

    This may reduce variance but can increase bias; not the most direct method.

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