Provides a bootstrapping and ensemble framework for supervised learning analyses using multiclass classification algorithms for modelling, prediction, and evaluation. Predicted classes are evaluated under metrics such as log loss, AUC, F1-score, Matthew's correlation coefficient, and accuracy. Discrimination and reliability plots visualize the classifier performances. The .632+ estimator is implemented for the log loss error rate.
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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