Rank features using logistic regression coefficients

Q: Rank features using logistic regression coefficients

This question evaluates understanding of feature scaling, interpretation of logistic regression coefficients as feature importance, and awareness of how regularization and tie-breaking can affect coefficient-based rankings.

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Question

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You are given a binary classification dataset:

X : a 2D array of shape (n_samples, n_features) containing numeric features
y : a 1D binary array of shape (n_samples,) with values in {0,1}
feature_names : a list of length n_features with the name of each column in X

Task

Normalize each feature column of X using z-score standardization:

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

where $\mu_j$ and $\sigma_j$ are the mean and standard deviation of feature $j$ computed on the training set.

Fit a logistic regression model on the normalized features.
Rank features by their learned coefficient values (largest to smallest), and return the top 3 feature names .

Output

Return a list of 3 strings: the names of the top-3 features.

Notes

Assume binary logistic regression (one coefficient per feature).
Specify how you handle ties and how you deal with regularization defaults in common libraries.

Rank features using logistic regression coefficients

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Rank features using logistic regression coefficients

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Task

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