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MLS-C01 Exploratory Data Analysis Practice Question

A team is analyzing a dataset with many categorical features that have high cardinality (e.g., ZIP code, user ID). They want to explore relationships between these features and a continuous target variable. Which approach is most appropriate for visualizing these relationships without overwhelming the viewer?

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

Group categories into top K levels and use a box plot for each group.

When dealing with high-cardinality categorical features, grouping the most frequent categories into a manageable number (e.g., top 10) and using box plots per group allows for clear visualization of the relationship with a continuous target. This approach reduces clutter and highlights differences in distributions. Option B is incorrect because Pearson correlation is designed for continuous variables, not categorical ones. Option C is incorrect: a scatter plot with color-coded categories becomes unreadable with many categories and does not effectively show distributional differences. Option D is incorrect because chi-square statistics assess association between two categorical variables, not between a categorical and a continuous variable.

Answer analysis

Option-by-option breakdown

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

  • Group categories into top K levels and use a box plot for each group.

    Why this is correct

    Aggregating categories makes the plot interpretable.

  • Compute a correlation matrix using Pearson correlation.

    Why it's wrong here

    Pearson correlation requires numerical data.

  • Create a scatter plot with each category as a different color.

    Why it's wrong here

    Scatter plots are not suitable for categorical data with high cardinality.

  • Use a heatmap to show pairwise chi-square statistics.

    Why it's wrong here

    Chi-square is for categorical-categorical, not continuous target.

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