Interpret Coefficients Carefully

Coefficients change log-odds

Logistic coefficients act on log-odds, not probability directly. Odds compare success probability with failure probability: probability 0.75 corresponds to odds 0.75/0.25 = 3, or three-to-one. Exponentiating a coefficient gives an odds multiplier for a one-unit feature increase while other included features stay fixed.

import numpy as np
for name, coef in zip(X.columns, model.coef_[0]):
    print(name, 'odds multiplier', np.exp(coef))

Feature scale affects coefficient magnitude, so a coefficient per dollar cannot be compared directly with one per percentage point. Correlated inputs also complicate individual explanations. Coefficients describe the fitted association under the model, not a causal guarantee.

Scenario: A coefficient of 0.69 yields an odds multiplier near 2. This means odds double per one-unit increase, not that probability rises by 69 percentage points. At different starting probabilities, the probability change differs.

Tip: Standardize numeric features when optimization or coefficient comparison benefits, and always explain the unit behind a one-unit change.