MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 500 features and 10,000 samples. After running a correlation matrix, they find that many feature pairs have correlation >0.95. What is the most appropriate next step to improve model performance?
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
✓
Apply principal component analysis (PCA) to reduce dimensionality.
PCA reduces dimensionality by transforming correlated features into uncorrelated principal components, addressing multicollinearity while retaining most of the variance. Option A is wrong: collecting more data does not reduce correlation between features. Option B is wrong: increasing regularization (e.g., L2) can mitigate multicollinearity effects, but with 500 features and many highly correlated pairs, PCA is more effective as a dimensionality reduction technique. Option D is wrong: removing all features with correlation >0.95 may discard useful information and is less systematic than PCA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Collect more training data to reduce the impact of correlated features.
Why it's wrong here
Adding more data does not reduce correlation between features.
- ✗
Increase the regularization parameter in the model.
Why it's wrong here
Regularization helps but is not the most appropriate first step when many features are correlated; PCA is more comprehensive.
- ✓
Apply principal component analysis (PCA) to reduce dimensionality.
Why this is correct
PCA reduces multicollinearity by transforming correlated features into orthogonal components.
- ✗
Remove all features with correlation above 0.95.
Why it's wrong here
Removing all such features may discard important information; consider PCA or regularization.
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