A Python-based toolkit for evaluating machine learning models using statistical tests. Includes implementations of McNemar Test, Two-Matched Samples t-Test, Friedman Test, Post-hoc Nemenyi Test, and visualization tools like boxplots. Designed to help researchers and practitioners compare model performance rigorously.
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A Python-based toolkit for evaluating machine learning models using statistical tests. Includes implementations of McNemar Test, Two-Matched Samples t-Test, Friedman Test, Post-hoc Nemenyi Test, and visualization tools like boxplots. Designed to help researchers and practitioners compare model performance rigorously.
mohamed-stifi/ML-Model-Evaluation-Statistical-Tests
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A Python-based toolkit for evaluating machine learning models using statistical tests. Includes implementations of McNemar Test, Two-Matched Samples t-Test, Friedman Test, Post-hoc Nemenyi Test, and visualization tools like boxplots. Designed to help researchers and practitioners compare model performance rigorously.
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