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

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

import classeval as clf

• All kinds of contributions are welcome!

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

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