Question 1,623 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 500 features and 100,000 observations. The target variable is binary. The dataset contains highly correlated features and some categorical variables with high cardinality. Which combination of techniques should the data scientist use to reduce dimensionality while preserving interpretability for EDA?
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 correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.
Correlation-based feature selection removes highly correlated features, reducing redundancy without distorting the original feature space, and target encoding converts high-cardinality categorical variables into numeric values based on the target mean, which preserves interpretability and avoids dimensionality explosion. Option A is incorrect because PCA reduces interpretability by transforming features into principal components and does not handle categorical variables directly. Option B is incorrect because mutual information is a feature selection method, but label encoding for high-cardinality categoricals can impose arbitrary ordinal relationships. Option C is incorrect because chi-squared test requires categorical features and is not suitable for high-dimensional numerical data; also, one-hot encoding high-cardinality categoricals leads to a drastic increase in dimensionality.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Apply Principal Component Analysis (PCA) to all features and then train a model on the top 50 components.
Why it's wrong here
PCA reduces interpretability and does not handle categorical features natively.
- ✗
Use mutual information to select top features and apply label encoding to categorical variables.
Why it's wrong here
Mutual information is useful but does not address high cardinality; label encoding implies ordinality.
- ✗
Use chi-squared test to select top features and one-hot encode categorical variables.
Why it's wrong here
Chi-squared is for categorical targets; one-hot encoding with high cardinality creates too many features.
- ✓
Apply correlation-based feature selection to remove highly correlated pairs, then use target encoding for high-cardinality categorical variables.
Why this is correct
Correlation filter reduces redundancy; target encoding converts categoricals to numeric without increasing dimensionality.
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Last reviewed: Jun 20, 2026
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