Normalize features and rank logistic coefficients

You are given a binary classification training dataset: X: a numeric feature matrix of shape (n_samples, n_features). feature_names: a list containing exactly n_features column names. y: a vector of length n_samples whose values are binary labels, either 0 or 1. Perform the following steps: Standardize each column of X independently using z-score normalization computed from the training data only: $$X'[:, j] = \frac{X[:, j] - \mu_j}{\sigma_j}$$ Here, for feature column j,…