https://github.com/bianconif/cutpointpy
ROC analysis and calculation of 'optimal' cut-points for binary classification tasks
https://github.com/bianconif/cutpointpy
binary-classification biomarker-analysis cutpoint cutpoint-optimization roc-analysis
Last synced: 7 days ago
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ROC analysis and calculation of 'optimal' cut-points for binary classification tasks
- Host: GitHub
- URL: https://github.com/bianconif/cutpointpy
- Owner: bianconif
- License: gpl-3.0
- Created: 2026-06-10T08:59:33.000Z (23 days ago)
- Default Branch: master
- Last Pushed: 2026-06-14T14:22:03.000Z (19 days ago)
- Last Synced: 2026-06-14T15:27:42.346Z (19 days ago)
- Topics: binary-classification, biomarker-analysis, cutpoint, cutpoint-optimization, roc-analysis
- Language: Python
- Homepage:
- Size: 26.4 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# cutpointpy
____________
A Python package for estimating the optimal cut-point of a predictor variable (feature) for a binary classification task. It is loosely inspired by [`cutpointr`](https://cran.r-project.org/web/packages/cutpointr/index.html), an optimal cut-point calculation package for [R](https://www.r-project.org/).
The main use cases of `cutpointpy` are:
- Optimal cut-point estimation
- Stability analysis of the estimated cut-points through bootstrapping
- Receiver-operating characteristic curve (ROC) analysis
## Installation
Coming up soon
## Structure
- `cutpointpy.core`: contains the main class (`CutpointCalculator`) with functions `find()` and `bootstrap()` for optimal cut-point estimation and stability anslysis/validation through bootstrapping.
- `cutpointpy.utils`: contains ancillary functions including methods for computing performance parameters on binary classification tasks (e.g. accuracy, sensitivity, specificity and AUC)
## Usage
Coming up soon.
## References
1. Hassanzad M., Hajian-Tilaki K. [Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review (2024) BMC Medical Research Methodology, 24 (1), art. no. 84](https://doi.org/10.1186/s12874-024-02198-2)
## Note
The package is still under development and not fully tested.