MLS-C01 Exploratory Data Analysis Practice Question
A company uses Amazon SageMaker to train a regression model. After training, the data scientist notices that the training loss decreases but validation loss increases after a few epochs. Which EDA technique could have helped predict this behavior?
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 learning curves showing training and validation loss over epochs
Plotting learning curves, which show training and validation loss over epochs, is the correct EDA technique to detect overfitting. The divergence where training loss decreases but validation loss increases is a clear sign of overfitting. Option A (box plots) helps identify outliers but does not directly indicate overfitting. Option C (residual plots) checks for homoscedasticity in regression, not overfitting. Option D (confusion matrix) is used for classification, not regression.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create box plots of each feature to identify outliers
Why it's wrong here
Box plots help identify outliers in feature distributions but do not track model performance over training iterations, so they cannot predict overfitting.
- ✓
Plot learning curves showing training and validation loss over epochs
Why this is correct
Learning curves plot training and validation loss over epochs; when validation loss starts increasing while training loss continues decreasing, it signals overfitting.
- ✗
Generate residual plots to check heteroscedasticity
Why it's wrong here
Residual plots are used to check for homoscedasticity and linearity assumptions in regression, not to monitor overfitting during training.
- ✗
Plot confusion matrix on the validation set
Why it's wrong here
Confusion matrices are used for classification models to show true positives, etc., not for regression models, and they do not show loss trends.
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