Question 374 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is working on a binary classification problem to predict loan default. The dataset has 200,000 samples and 50 features. The target variable is imbalanced: 5% default, 95% non-default. The scientist trains a logistic regression model and achieves 95% accuracy, but the recall for the default class is only 20%. The business requires that at least 70% of actual defaults be identified (recall >= 0.7). Which approach should the scientist take to improve recall without significantly sacrificing precision?
⚠ Common exam trap
Watch out — candidates often choose threshold adjustment (Option C) as a quick fix for recall, failing to recognize that it is a superficial change that does not improve the model's learned decision boundary and typically sacrifices precision disproportionately, whereas SMOTE addresses the root cause of imbalance in the training data.
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 oversampling techniques like SMOTE to create synthetic samples of the minority class
SMOTE (Synthetic Minority Oversampling Technique) creates synthetic samples for the minority class by interpolating between existing minority instances, which increases the representation of the default class in the training data. This directly addresses the low recall (20%) by providing the logistic regression model with more balanced class distributions, enabling it to learn decision boundaries that capture more true positives without discarding majority class information. Unlike simple oversampling, SMOTE reduces overfitting risk by generating novel samples rather than duplicating existing ones, which helps maintain precision while improving 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.
- ✗
Use random undersampling of the majority class to balance the dataset
Why it's wrong here
Undersampling can improve recall but often at the cost of losing information and reducing precision.
- ✓
Use oversampling techniques like SMOTE to create synthetic samples of the minority class
Why this is correct
SMOTE generates synthetic minority samples, helping the model learn better decision boundaries for the minority class, improving recall with less precision loss.
- ✗
Change the decision threshold to 0.3
Why it's wrong here
Lowering the threshold increases recall but can significantly reduce precision, which may not be acceptable.
- ✗
Increase the regularization strength (C) in logistic regression
Why it's wrong here
Stronger regularization reduces model complexity, which typically worsens recall.
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Last reviewed: Jun 24, 2026
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