MLS-C01 Exploratory Data Analysis Practice Question
A company is preparing a dataset for training a binary classification model. The dataset has a severe class imbalance (1% positive class). The data scientist wants to understand the impact of this imbalance on model performance before sampling. Which exploratory analysis step is MOST critical?
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
✓
Plot the distribution of each feature separately for the positive and negative classes.
The most critical step because plotting the distribution of each feature separately for the positive and negative classes allows the data scientist to visually assess class separability, overlap, and feature behavior under severe imbalance. This insight directly informs the impact of imbalance on model performance before any sampling. Options A, B, and C are less critical at this stage: correlation with the target (A) does not reveal class-level distributions; missing values and outliers (B) are important but not specific to understanding imbalance impact; PCA (C) is a dimensionality reduction technique that may obscure per-feature patterns and is not necessary for initial exploratory analysis of class distributions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Compute the correlation matrix of all features with the target variable.
Why it's wrong here
Correlation may be low due to imbalance, not directly informative.
- ✗
Check for missing values and outliers in the dataset.
Why it's wrong here
Important but not the most critical for imbalance.
- ✗
Perform PCA and visualize the first two principal components colored by class.
Why it's wrong here
PCA may not show separation in imbalanced data.
- ✓
Plot the distribution of each feature separately for the positive and negative classes.
Why this is correct
Overlapping distributions indicate difficulty in classification.
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