MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model currently achieves 94% accuracy but a recall of only 0.10 on the positive class. Which TWO strategies should the data scientist consider to improve recall without significantly sacrificing precision? (Choose 2.)
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
✓
Assign higher class weights to the positive class in the loss function.
Assigning higher class weights to the positive class in the loss function (option C) penalizes misclassifications of the minority class more heavily, forcing the model to focus on positive examples. Oversampling the minority class using SMOTE (option E) generates synthetic positive samples, improving the model's ability to learn decision boundaries for the positive class. Both techniques directly address class imbalance without discarding data. Option A (undersampling) may remove useful negative samples, harming overall performance. Option B (increasing regularization) reduces overfitting but does not specifically improve recall. Option D (using a deeper network) may increase overfitting and does not target recall directly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Undersample the majority class to match the minority class size.
Why it's wrong here
Undersampling discards many negative samples, potentially losing useful information.
- ✗
Increase the regularization strength to reduce overfitting.
Why it's wrong here
Regularization does not specifically address class imbalance or recall.
- ✓
Assign higher class weights to the positive class in the loss function.
Why this is correct
Higher weight for positive class penalizes false negatives, improving recall.
- ✗
Use a deeper neural network with more layers.
Why it's wrong here
Adding layers may increase capacity but does not target recall improvement for imbalanced data.
- ✓
Oversample the minority class using SMOTE.
Why this is correct
SMOTE generates synthetic positive samples, balancing the dataset and improving recall.
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.