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doi: 10.5281/zenodo.5905702 , 10.5281/zenodo.7003980 , 10.5281/zenodo.7883974 , 10.5281/zenodo.1157172 , 10.5281/zenodo.16886677 , 10.5281/zenodo.15149084 , 10.5281/zenodo.8014450 , 10.5281/zenodo.13946501 , 10.5281/zenodo.17361511 , 10.5281/zenodo.6498204 , 10.5281/zenodo.7595034 , 10.5281/zenodo.7442626 , 10.5281/zenodo.14645845
doi: 10.5281/zenodo.5905702 , 10.5281/zenodo.7003980 , 10.5281/zenodo.7883974 , 10.5281/zenodo.1157172 , 10.5281/zenodo.16886677 , 10.5281/zenodo.15149084 , 10.5281/zenodo.8014450 , 10.5281/zenodo.13946501 , 10.5281/zenodo.17361511 , 10.5281/zenodo.6498204 , 10.5281/zenodo.7595034 , 10.5281/zenodo.7442626 , 10.5281/zenodo.14645845
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and accurate evaluation of a large variety of classifiers.
If you use this software, please cite it as below.
python, F-score, confusion matrix, Accuracy
python, F-score, confusion matrix, Accuracy
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