---
id: betacal
version: "1.1.0"
license: MIT
license_treatment: permissive
maintenance: dormant
---

# betacal — Beta calibration

License: permissive · Maintenance: dormant · Downloads: 423.8K/mo

## What it is and what it does

Betacal provides a BetaCalibration class that fits probabilistic calibration models to classifier predictions. It takes predicted probabilities from a classifier and learns a transformation that maps them to better-calibrated confidence estimates, useful when you need reliable probability outputs rather than just class predictions.

The package depends on numpy for numerical operations and scikit-learn for machine learning utilities. It is designed as a post-processing step: you train your classifier separately, then use betacal to recalibrate its probability outputs on a validation set before applying it to test data.

Use it for:

- Recalibrate neural network or ensemble classifier probabilities to improve confidence reliability in production systems.
- Adjust predicted probabilities from imbalanced classifiers to better reflect true class likelihood.
- Post-process classifier outputs when you need well-calibrated confidence scores for decision-making.
- Improve probability estimates from models trained on small datasets where calibration is often poor.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Fits beta calibration models to classifier outputs, transforming predicted probabilities into better-calibrated confidence estimates.

Yes, if you need classifier calibration and can tolerate dormant maintenance. The package is lightweight, has no known vulnerabilities, and solves a specific problem well. However, verify compatibility with your numpy and scikit-learn versions before relying on it in production, given the last release was April 2021.

## Install

pip install betacal
uv add betacal
poetry add betacal

## Installing betacal

Before you install: Low friction install with only numpy and scikit-learn as dependencies. Dormant maintenance since April 2021; last commit February 2024 indicates minimal active development, though the repository remains available.

License in practice: MIT license permits commercial and private use with minimal restrictions; you must include a copy of the license and copyright notice.

Quickstart:

pip install betacal
import betacal
from betacal import BetaCalibration
bc = BetaCalibration()
bc.fit(y_true, y_pred)
calibrated_probs = bc.predict(y_pred)

Verify before relying:

- Whether the package works with modern scikit-learn and numpy versions (last release April 2021)
- Python version compatibility (requires_python field is empty in metadata)

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: dormant
- Downloads: 423.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

classifier probability calibration, beta calibration model, calibrate prediction confidence, probability calibration python, classifier confidence adjustment, calibration machine learning, calibration, classifier-tuning

[View on SkillFed](https://skillfed.io/packages/betacal) · [View on PyPI](https://pypi.org/project/betacal/)