MLS-C01 Exploratory Data Analysis Practice Question
A machine learning team is analyzing a dataset with 10,000 rows and 200 features. They suspect data leakage due to time-based features. Which THREE EDA checks should they perform?
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 distribution of each feature in training vs. test sets
Plotting the distribution of each feature in training vs. test sets helps detect data leakage if the distributions differ significantly (e.g., train contains future data). Option C is correct because checking if the dataset is sorted by time and if any feature uses future information directly addresses time-based leakage. Option D is correct because comparing feature correlations with the target in training and test sets can reveal leakage if correlations are abnormally high in training due to future data. Option B is wrong because PCA is a dimensionality reduction technique and does not directly detect leakage. Option E is wrong because k-means clustering is an unsupervised method and not suitable for leakage detection in this context.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Plot distribution of each feature in training vs. test sets
Why this is correct
Plotting the distribution of each feature in training vs. test sets helps detect data leakage if the distributions differ significantly (e.g., train contains future data).
- ✗
Apply PCA and check if first two components separate train/test
Why it's wrong here
PCA is a dimensionality reduction technique and does not directly detect leakage.
- ✓
Check whether the dataset is sorted by time and if any feature uses future information
Why this is correct
Checking if the dataset is sorted by time and if any feature uses future information directly addresses time-based leakage.
- ✓
Compare feature correlations with target in training and test sets
Why this is correct
Comparing feature correlations with the target in training and test sets can reveal leakage if correlations are abnormally high in training due to future data.
- ✗
Perform k-means clustering on the whole dataset
Why it's wrong here
K-means clustering is an unsupervised method and not suitable for leakage detection in this context.
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Written by Johnson Ajibi, MSc IT Security
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