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Data and AI


Evaluate the performance of ML algorithms

The outputs and results of machine learning algorithms are usually in the form of confusion matrices. PyCM is an open source python library for evaluating, quantifying, and reporting the results of machine learning algorithms systematically. PyCM provides a wide range of confusion matrix evaluation metrics to process and evaluate the performance of machine learning algorithms comprehensively. This open source library allows users to compare different algorithms in order to determine the optimal one based on their preferences and priorities. In addition, the evaluation can be reported in different formats. PyCM has been widely used as a standard and reliable post-processing tool in the most reputed open-source AI projects like TensorFlow similary, Google's scaaml, torchbearer, and CLaF.

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This project was funded through the NGI Assure Fund, a fund established by NLnet with financial support from the European Commission's Next Generation Internet programme, under the aegis of DG Communications Networks, Content and Technology under grant agreement No 957073.

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