Explain interpretability and coefficient analysis in Logistic Regression

Logistic Regression is highly interpretable because coefficients directly describe how features influence prediction probabilities.

Interpretability is one of the biggest reasons Logistic Regression remains dominant in regulated industries.

Unlike black-box models such as deep neural networks, Logistic Regression provides transparent mathematical relationships between input variables and prediction outcomes.

The Logistic Regression equation:

log(p / (1-p)) = b0 + b1x1 + b2x2 + ... + bnxn

Key interpretation: