MLS-C01 Exploratory Data Analysis Practice Question
A data scientist runs a logistic regression and obtains a model with 95% accuracy on the training set. However, the model performs poorly on the test set. Which exploratory data analysis step should have been performed to identify this issue?
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
✓
Checking for class imbalance in the target variable
Checking for class imbalance is critical because it can cause a model to predict the majority class and still achieve high accuracy, but fail on the minority class in unseen data. Option A (correlation matrix) is wrong because it helps with multicollinearity, not class imbalance. Option B (log transformation) is wrong because it addresses skewness in features, not class imbalance. Option D (heatmap of missing values) is wrong because it shows missing data patterns, not class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Generating a correlation matrix of features
Why it's wrong here
A correlation matrix helps identify multicollinearity among features, but it does not address class imbalance, which is the root cause of the described problem.
- ✗
Log transformation of skewed features
Why it's wrong here
Log transformation addresses skewed distributions of features, not class imbalance.
- ✓
Checking for class imbalance in the target variable
Why this is correct
Checking for class imbalance is the correct step because a model can achieve high training accuracy by simply predicting the majority class, but fails on the minority class in the test set.
- ✗
Creating a heatmap of missing values
Why it's wrong here
A heatmap of missing values shows patterns of missing data, but does not help with class imbalance.
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