> For the complete documentation index, see [llms.txt](https://docs.algenta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.algenta.ai/machine-learning/ml-calibration.md).

# Probability calibration

Isotonic (PAV) and z-score affine calibration with reliability diagnostics for turning raw model scores into trustworthy probabilities.

`calibration` maps raw model scores to trustworthy probabilities or magnitudes. A model can rank well and still be badly calibrated — betting sizes, thresholds, and expected-value math all need calibrated outputs.

## Functions

| Function                                                                | What it does                                                                                                                                                                                                         |
| ----------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `isotonic_fit(x, y)` / `isotonic_transform(...)`                        | Isotonic regression via Pool Adjacent Violators (weighted tie handling, sklearn-equivalent). Monotonic, non-parametric — the standard for calibrating regression outputs and probabilities without assuming a shape. |
| `platt_fit(scores, labels, max_iter)` / `platt_transform(a, b, scores)` | Platt scaling: logistic calibration with Platt's target smoothing — best when the miscalibration is sigmoidal and data is scarce.                                                                                    |
| `calibration_curve(probs, labels, n_bins)`                              | Reliability diagram data: per-bin predicted vs observed frequency and counts. Pair with the ECE/Brier metrics in `ml_metrics_extended`.                                                                              |

## Choosing isotonic vs Platt

Isotonic fits any monotone distortion but needs more data (it can overfit below \~1k samples). Platt is two parameters — robust when small. When in doubt, compare both on a held-out reliability curve.

## Verification

Verified against sklearn's isotonic regression, including weighted-tie pooling cases that break naive PAV implementations.

**Next:** promote what you trained with [statistical model comparison](/machine-learning/ml-model-comparison.md), or browse the [engine catalog](/engine-runtime/catalog.md).


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