MLS-C01 Modeling Practice Question
A data scientist is building a model to predict customer churn. The dataset includes both numerical features (e.g., account age, usage minutes) and categorical features (e.g., region, plan type). The data scientist wants to use a linear classifier. Which feature engineering step is required before training?
⚠ Common exam trap
Many exam-takers assume normalization (A) is the most critical step for linear models, overlooking that categorical features must be converted to numerical form before any linear classifier can process them.
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
✓
One-hot encode categorical features
Linear classifiers (e.g., logistic regression, linear SVM) require numerical input and cannot directly process categorical text labels. One-hot encoding converts each categorical feature into binary indicator columns, allowing the linear model to learn separate weights for each category. Without this step, the model would either fail to train or treat categorical strings as ordinal values, which is mathematically invalid for linear decision boundaries.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Normalize numerical features
Why it's wrong here
Normalization is helpful but not strictly required; linear models can handle raw values.
- ✗
Impute missing values
Why it's wrong here
Imputation is important but not the only required step; encoding is mandatory for categoricals.
- ✗
Remove outliers
Why it's wrong here
Outlier removal is optional and not a required step for linear models.
- ✓
One-hot encode categorical features
Why this is correct
Linear models require numerical input; one-hot encoding converts categories to binary vectors.
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