MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is analyzing a dataset with a continuous target variable and suspects that the relationship between a predictor and the target is non-linear. Which THREE techniques can the scientist use to explore and model this non-linearity?
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
✓
Add polynomial features (e.g., x^2, x^3) and check if model performance improves.
Options C, D, and E are correct. Adding polynomial features (e.g., x^2, x^3) allows a linear model to capture non-linear relationships. Decision tree regressors naturally model non-linear interactions between predictors and the target. A scatter plot with a LOESS smooth curve visually reveals non-linear patterns in the data. Option A (logistic regression) is incorrect because it is for binary classification, not for exploring non-linearity with a continuous target. Option B (Pearson correlation) only measures linear relationships, so it is not suitable for detecting non-linearity.
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 logistic regression to binarize the target.
Why it's wrong here
Logistic regression is for classification, not for exploring non-linearity with continuous target.
- ✗
Compute the Pearson correlation coefficient between the predictor and target.
Why it's wrong here
Pearson correlation only measures linear relationships.
- ✓
Add polynomial features (e.g., x^2, x^3) and check if model performance improves.
Why this is correct
Polynomial features capture non-linearity in linear models.
- ✓
Fit a decision tree regressor and examine feature importance.
Why this is correct
Decision trees model non-linear relationships.
- ✓
Create a scatter plot and overlay a LOESS (local regression) smooth curve.
Why this is correct
LOESS visually shows non-linear patterns.
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