Platform empowering data science professionals with comprehensive tools to evaluate classifier performance in imbalanced classification tasks
accurate performance evaluation metrics
visualization tools for classifier performance
benchmarks for imbalanced data
Comprehensive suite of features including visualizations of confusion matrices, ROC-AUC, and AUC-PR curves, plus benchmarks and guidelines for acceptable false positive rates for imbalanced datasets
accurate performance evaluation metrics
visualization tools for classifier performance
benchmarks for imbalanced data
decision-making support for model optimization
With ClassifEval, you can user registration and login, Upload prediction CSV files, Confusion matrix visualization, and ROC-AUC curve chart.
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