Question 453 of 1,672
MLS-C01 Modeling Practice Question
A data science team is training a binary classification model using Amazon SageMaker. The dataset is highly imbalanced (95% negative class, 5% positive class). The team wants to maximize the F1 score. Which built-in SageMaker algorithm is most appropriate?
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
✓
XGBoost
XGBoost supports scale_pos_weight to handle class imbalance, directly optimizing for F1. Linear Learner with balanced class weights can also help but typically optimizes log loss. K-Means is unsupervised. PCA is for dimensionality reduction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Linear Learner
Why it's wrong here
Linear Learner can use balanced class weights but does not directly optimize F1.
- ✓
XGBoost
Why this is correct
XGBoost has scale_pos_weight parameter to handle imbalance and can optimize for F1.
- ✗
PCA
Why it's wrong here
PCA is for dimensionality reduction, not classification.
- ✗
K-Means
Why it's wrong here
K-Means is an unsupervised clustering algorithm, not suitable for classification.
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Last reviewed: Jun 20, 2026
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