MLS-C01 Modeling Practice Question
A financial services company is building a model to detect fraudulent credit card transactions. The dataset contains 1 million transactions, with only 0.1% labeled as fraud. The data scientist trains a logistic regression model on the raw dataset and obtains the following results on a held-out test set: accuracy = 99.8%, precision = 50%, recall = 60%, F1 = 0.545. The business requirement is to maximize recall while keeping precision above 80%. Which course of action should the data scientist take to improve the model?
⚠ Common exam trap
The MLS-C01 exam often tests the misconception that resampling (undersampling or oversampling) is always the best first step for imbalance, when in fact cost-sensitive learning via class weights is often more effective and stable for linear models like logistic regression.
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
✓
Train the model with class weights inversely proportional to class frequencies
Assigning class weights inversely proportional to class frequencies penalizes misclassifications of the minority class (fraud) more heavily during training. This directly addresses the severe class imbalance (0.1% fraud) by forcing the logistic regression model to learn decision boundaries that improve recall, while the weight ratio can be tuned to maintain precision above 80%. Unlike naive resampling, this approach preserves the original data distribution and avoids information loss.
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 random undersampling of the majority class to balance the dataset
Why it's wrong here
Undersampling discards data and may reduce model performance, especially with a large majority class.
- ✗
Collect more historical transaction data and retrain the model
Why it's wrong here
More data might help, but it's not guaranteed to improve precision dramatically, and the timeline is uncertain.
- ✓
Train the model with class weights inversely proportional to class frequencies
Why this is correct
Class weights help the model focus on the minority class, often improving precision and recall.
- ✗
Apply L2 regularization with a higher penalty to reduce overfitting
Why it's wrong here
Regularization reduces overfitting but does not directly address class imbalance or precision.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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