Question 321 of 1,672
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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