MLS-C01 Exploratory Data Analysis Practice Question
Which THREE of the following are best practices when performing exploratory data analysis on a dataset with both numerical and categorical features?
⚠ Common exam trap
Watch out — candidates often assume label encoding is harmless for categorical features, but it imposes an artificial order that can distort model behavior, especially in tree-based models that rely on split points.
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
✓
Check the proportion of missing values for each feature.
Checking the proportion of missing values for each feature is a fundamental step in exploratory data analysis (EDA). It helps identify data quality issues, such as systematic missingness, which can bias downstream modeling and inform decisions about imputation strategies or feature exclusion.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Check the proportion of missing values for each feature.
Why this is correct
Missing value analysis is a key EDA step.
- ✓
Compute pairwise correlation coefficients between numerical features.
Why this is correct
Correlation analysis detects multicollinearity.
- ✗
Encode all categorical features using label encoding for simplicity.
Why it's wrong here
Label encoding can introduce false ordinal relationships.
- ✗
Include all categorical features with high cardinality as-is in the model.
Why it's wrong here
High cardinality features often need encoding or grouping.
- ✓
Visualize the distribution of numerical features using histograms and box plots.
Why this is correct
Visualization helps identify skewness and outliers.
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