Fit logistic regression and return top features

Q: Fit logistic regression and return top features

This question evaluates understanding of binary logistic regression, coefficient-based feature importance, and practical model-fitting considerations such as intercept inclusion and input shape handling.

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Question

You are given:

X : a 2D numeric array where each row is a feature and each column is an observation (shape: n_features x n_samples ).
feature_names : a list of length n_features .
y : a binary outcome array of length n_samples with values in {0, 1}.

Task:

Fit a (binary) logistic regression model to predict y from X .
- Include an intercept.
- No regularization unless explicitly stated.
Rank features by the absolute value of their fitted coefficients (exclude the intercept).
Return the top 3 feature names by |coef| (break ties by lexicographic order of feature_names ).

Output:

A list of 3 strings (feature names).

Fit logistic regression and return top features

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Solution

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