MLS-C01 Modeling Practice Question
A data scientist is building a fraud detection model using a highly imbalanced dataset. The model uses a random forest classifier. The recall for the minority class is 0.6, and precision is 0.9. The business requires recall above 0.8. Which action should the data scientist take to improve recall?
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
✓
Decrease the probability threshold for classifying a transaction as fraudulent.
Decreasing the probability threshold for classifying a transaction as fraudulent increases recall because more transactions are predicted as positive, capturing more true positives at the cost of precision. Option A (feature selection) might remove noisy features but could inadvertently eliminate informative ones, potentially reducing recall. Option B (increasing maximum depth of trees) increases model complexity and risk of overfitting without directly improving recall. Option C (increasing class weight for the minority class) can help the model focus on the minority class, but if recall is still insufficient, threshold adjustment is a more direct approach. Option E (increasing number of trees) reduces variance and improves generalization but does not directly increase recall.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Perform feature selection to remove noisy features.
Why it's wrong here
(feature selection) may reduce recall.
- ✗
Increase the maximum depth of the trees.
Why it's wrong here
(increase maximum depth) could lead to overfitting and not necessarily improve recall.
- ✗
Increase the class weight for the minority class in the algorithm.
Why it's wrong here
(class weights) can help but is already used.
- ✓
Decrease the probability threshold for classifying a transaction as fraudulent.
Why this is correct
Decreasing the classification threshold for the positive class increases recall (more positives predicted) at the cost of precision.
- ✗
Increase the number of trees in the random forest.
Why it's wrong here
(more trees) reduces variance, may not improve recall.
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