Calibration Analysis

Brier score decomposition splits model predictions into reliability, resolution, and uncertainty components to quantify calibration quality across prediction bins. You provide a prediction array and outcomes to compute these metrics and identify exactly where the model is wrong.

A calibration curve plots predicted probabilities against realized outcomes to visualize model reliability. By applying it across regimes and time-based slices, you can perform conditional calibration to identify targeted areas for improving model predictions in production ML environments.

Daily calibration reporting and PnL attribution workflows monitor model health in production by systematically analyzing predictions versus realized outcomes. You compute Brier decomposition, information ratio, and conditional calibration to quantify calibration gaps and track model degradation over time.

Yes, conditional calibration analyzes model predictions across regimes and time-based slices. By applying Brier score decomposition and calibration curves to these segments, you identify specific conditions where the model underperforms and needs targeted improvements.

Calibration analysis requires a prediction array of probabilities and corresponding realized outcomes, plus a binning strategy. These inputs enable Brier score decomposition, information ratio calculation, and calibration curve construction to evaluate model reliability and resolution.