Metadata-Version: 2.4
Name: medevalkit
Version: 0.1.2
Summary: A toolkit for evaluating machine learning models for healthcare applications.
Author: Wesley Yeung
Author-email: corona-monody-7e@icloud.com
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: lifelines
Requires-Dist: matplotlib
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Requires-Dist: scikit-survival
Requires-Dist: seaborn
Dynamic: author
Dynamic: author-email
Dynamic: description
Dynamic: description-content-type
Dynamic: license-file
Dynamic: requires-dist
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# MedEvalKit

**MedEvalKit** is a modular and extensible Python toolkit for evaluating machine learning models, especially in healthcare applications. It provides unified APIs for computing metrics, calibration curves, bootstrap confidence intervals, and plotting diagnostic curves.

## Features

- Binary and multiclass classification support
- Threshold optimization
- Calibration error estimation
- Bootstrap confidence intervals
- ROC, PR, and calibration plotting
- Simulate results at different incidence rates

## Installation

```bash
pip install medevalkit
```

## Usage Example

```python
from medevalkit import Evaluate

clf.fit(X_train, y_train)

evaluator = Evaluate(clf, X_test, y_test, classification=True, threshold=threshold)
report = evaluator.generate_report(bootstrap=True)
print(report['text_report'])
```

## GitHub Repo

Visit https://github.com/wesleyyeung/medevalkit for more examples.

## License

This project is licensed under the MIT License.
