MLS-C01 Modeling Practice Question

A company wants to deploy a machine learning model that predicts customer churn. The model must provide interpretable predictions to explain why a customer is likely to churn. Which algorithm is most appropriate?

⚠ Common exam trap

The MLS-C01 exam often tests the trade-off between model accuracy and interpretability, where candidates mistakenly choose a high-performance black-box model (like gradient boosting or neural networks) without recognizing that the question explicitly prioritizes interpretability over raw predictive power.

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

✓

Decision tree

Decision trees are inherently interpretable because they produce a clear, rule-based structure that shows exactly which features and thresholds lead to a churn prediction. This white-box nature allows stakeholders to trace the reasoning for each prediction, meeting the requirement for interpretability without needing post-hoc explanation methods.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Gradient boosting machine
Why it's wrong here
Gradient boosting is less interpretable than a single tree.
✗
Support vector machine (SVM)
Why it's wrong here
SVM is not easily interpretable.
✓
Decision tree
Why this is correct
Decision trees are highly interpretable.
✗
Deep neural network
Why it's wrong here
Deep neural networks are not easily interpretable.

Go deeper

MLS-C01 Modeling Practice Question

Why each option matters

Option-by-option breakdown

Related to this question