MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a model that predicts loan defaults. The model uses features like income, credit score, and debt-to-income ratio. After deployment, the model's performance degrades over time. Which concept best describes this phenomenon?
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
✓
Model drift
Model drift is the correct answer because it is the general term for degradation in model performance over time, often caused by changes in data distributions or relationships between features and the target. This phenomenon includes both data drift (changes in input distribution) and concept drift (changes in the relationship between inputs and the target). Option A (Data drift) is a specific type of model drift focusing on input features, not the overall degradation. Option B (Concept drift) is another specific type concerning the target relationship. Option C (Overfitting) is a training-time issue where the model fits noise, not a time-dependent degradation after deployment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data drift
Why it's wrong here
Data drift refers to changes in the input feature distribution.
- ✗
Concept drift
Why it's wrong here
Concept drift refers to changes in the relationship between inputs and output.
- ✗
Overfitting
Why it's wrong here
Overfitting is not a time-dependent phenomenon.
- ✓
Model drift
Why this is correct
Model drift is the degradation of model performance over time.
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