Question 841 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on a dataset with 100,000 positive samples and 1,000 negative samples. The model achieves 99% accuracy on the test set but a very low F1 score. What is the most likely cause?
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
✓
The model predicts the majority class most of the time due to class imbalance
The accuracy is high because the model predicts the majority class (positive) most of the time, but the F1 score is low because it fails to identify the minority class (negative) correctly. This is a classic symptom of class imbalance where the model is biased toward the majority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The test set contains only positive samples
Why it's wrong here
If the test set only contained positives, accuracy could be 100% but F1 would be undefined or 0.
- ✗
The model is overfitting due to too many features
Why it's wrong here
Overfitting would cause high training accuracy but low test accuracy; here test accuracy is high.
- ✗
The model is underfitting due to insufficient training
Why it's wrong here
Underfitting would cause low accuracy on both training and test sets.
- ✓
The model predicts the majority class most of the time due to class imbalance
Why this is correct
Class imbalance causes the model to be biased toward the majority class, leading to high accuracy but low F1.
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Last reviewed: Jun 20, 2026
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