classeval

The library classeval is developed to evaluate the models performance of any kind of two-class or multi-class model. classeval computes many scoring measures in case of a two-class clasification model. Some measures are utilized from sklearn, among them AUC, MCC, Cohen kappa score, matthews correlation coefficient, whereas others are custom. This library can help to consistenly compare the output of various models. In addition, it can also give insights in tuning the models performance as the the threshold being used can be adjusted and evaluated. The output of classeval can subsequently plotted in terms of ROC curves, confusion matrices, class distributions, and probability plots. Such plots can help in better understanding of the results.

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Documentation pages

On the documentation pages you can find more information about classeval with examples.

Install classeval from PyPI

pip install classeval     # normal install
pip install -U classeval  # update if needed

Import classeval package

import classeval as clf

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Examples

Example: Evaluate Two-class model

Example: Evaluate multi-class model

Example: Model performance tweaking

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Contribute

• All kinds of contributions are welcome!

Citation

Please cite classeval in your publications if this is useful for your research. See column right for citation information.

Maintainer

• Erdogan Taskesen, github: erdogant
• Contributions are welcome.
• If you wish to buy me a Coffee for this work, it is very appreciated :)