Explain probability calibration and reliability in Logistic Regression

Probability calibration ensures predicted probabilities accurately reflect real-world event likelihoods.

One of the greatest strengths of Logistic Regression is its probabilistic interpretation. However, even accurate classifiers can produce poorly calibrated probabilities.

A model is well-calibrated if:

Calibration is critical in high-stakes domains because decisions depend on probability confidence.

Examples:

Poor calibration creates dangerous systems.…