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MLS-C01 Modeling Practice Question

An e-commerce company uses a linear regression model to predict customer lifetime value (LTV). The model shows high variance on the test set, with training RMSE much lower than test RMSE. Which of the following is the MOST effective approach to reduce overfitting?

⚠ Common exam trap

The MLS-C01 exam often tests the misconception that adding more data always reduces overfitting, but the trap here is that duplicating existing samples (Option D) does not provide new, diverse examples and therefore fails to address the root cause of high variance.

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 L2 regularization (Ridge regression)

High variance (low training RMSE, high test RMSE) indicates overfitting. L2 regularization (Ridge regression) adds a penalty proportional to the square of the coefficients, shrinking them toward zero without eliminating them, which reduces model complexity and improves generalization. This directly addresses overfitting by constraining the model's sensitivity to noise in the training data.

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 L2 regularization (Ridge regression)

    Why this is correct

    L2 regularization shrinks coefficients and reduces variance.

  • Use a polynomial kernel in a support vector regressor

    Why it's wrong here

    Polynomial kernel can increase complexity and overfitting.

  • Add more features, including interaction terms

    Why it's wrong here

    Adding features may increase variance.

  • Increase training data size by duplicating existing samples

    Why it's wrong here

    Duplicating data does not add new information and may not reduce overfitting.

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