MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with 500 features and 10,000 rows. The target variable is binary. After training a logistic regression model, the coefficients show many non-zero values but the model has low accuracy on the test set. Which EDA step should the data scientist perform next to improve model performance?
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
✓
Use correlation analysis or mutual information to select the most relevant features.
With 500 features and low accuracy, the model likely suffers from overfitting due to irrelevant or redundant features. Correlation analysis or mutual information helps select the most relevant features, reducing noise and improving generalization. Option A (PCA) reduces dimensionality but creates uninterpretable components and may lose feature relationships, not directly addressing irrelevant features. Option B (collect more data) may help but does not solve the core issue of irrelevant features. Option C (normalization) only scales features, not reduce them, and logistic regression is not sensitive to scale if coefficients are interpreted carefully; overfitting is more likely due to too many features.
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 reduce dimensionality.
Why it's wrong here
PCA creates new features that may be less interpretable and does not directly address overfitting from irrelevant features.
- ✗
Collect more training data to improve generalization.
Why it's wrong here
Adding data may not help if the model is overfitting to irrelevant features.
- ✗
Normalize the features using StandardScaler.
Why it's wrong here
Scaling does not reduce the number of features or address irrelevant features.
- ✓
Use correlation analysis or mutual information to select the most relevant features.
Why this is correct
Feature selection removes irrelevant features, reducing noise and overfitting.
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