MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is using Amazon SageMaker Data Wrangler to explore a dataset. They notice that a feature has a very high correlation (0.95) with the target variable. What should they do to avoid overfitting?
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
✓
Remove the feature from the dataset
The feature with a 0.95 correlation to the target is likely leaking target information (data leakage), which would cause the model to overfit on training data but fail on new data. Removing the feature (Option D) directly addresses the leakage. Option A (L2 regularization) helps with overfitting from noisy features but does not remove the leaked information. Option B (PCA) reduces dimensionality but the leak would still be present in the principal components. Option C (StandardScaler) only normalizes the feature, not removes it. Therefore, the best action is to remove the feature.
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 L2 regularization in the model
Why it's wrong here
Incorrect: Regularization reduces coefficient magnitude but does not address leakage.
- ✗
Apply PCA to reduce dimensionality
Why it's wrong here
Incorrect: PCA may still contain the leaking information in principal components.
- ✗
Standardize the feature using StandardScaler
Why it's wrong here
Incorrect: Scaling does not change correlation or prevent overfitting.
- ✓
Remove the feature from the dataset
Why this is correct
Correct: High correlation with target can indicate data leakage; removing is safest.
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