Normalize features and rank logistic coefficients

Q: Normalize features and rank logistic coefficients

This question evaluates understanding of feature preprocessing (z‑score normalization), linear logistic regression fitting and coefficient-based feature ranking, along with practical considerations such as handling zero-variance features, intercept treatment, and regularization; it is commonly asked to test applied skills in producing comparable feature scales and interpretable model weights. Category/domain: Machine Learning; abstraction level: applied, implementation-focused data science task appropriate for Data Scientist interviews.

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Question

You are given a binary classification training dataset:

X : a 2D array of shape (n_samples, n_features) containing numeric features.
feature_names : a list of length n_features with the feature names.
y : a 1D binary array (0/1) of length n_samples.

Task:

Normalize each feature column of X using z-score standardization based on the training set:

$X'_{:,j} = \frac{X_{:,j} - \mu_j}{\sigma_j}$

(where $\mu_j$ and $\sigma_j$ are the mean and standard deviation of feature $j$ over the training data.)

Fit a (linear) logistic regression model on the normalized features.
Extract the fitted coefficients and rank features by coefficient value (largest to smallest). Return the top 3 feature names .

Clarify any modeling choices needed to make this well-defined (e.g., intercept, regularization, handling zero-variance features).

Normalize features and rank logistic coefficients

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