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MLS-C01 Modeling Practice Question

A machine learning engineer is evaluating a multi-class classification model that predicts product categories. The model outputs probabilities for 10 classes. The engineer wants to improve the model's calibration so that the predicted probabilities reflect the true likelihood of each class. Which THREE techniques can help?

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

Use temperature scaling

Platt scaling and isotonic regression are common calibration methods for classification models. Temperature scaling is a variant of Platt scaling for neural networks. Using a different loss function like cross-entropy helps but is not a calibration technique per se.

Answer analysis

Option-by-option breakdown

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

  • Use temperature scaling

    Why this is correct

    Temperature scaling adjusts the softmax temperature to improve calibration for neural networks.

  • Apply isotonic regression

    Why this is correct

    Isotonic regression is a non-parametric calibration method that can improve calibration.

  • Increase model complexity

    Why it's wrong here

    Increasing complexity may worsen calibration due to overconfidence.

  • Apply Platt scaling

    Why this is correct

    Platt scaling fits a logistic regression to the model's outputs to calibrate probabilities.

  • Use focal loss

    Why it's wrong here

    Focal loss addresses class imbalance but does not directly calibrate probabilities.

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Last reviewed: Jun 20, 2026

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