splendid-package: splendid: SuPervised Learning ENsemble for Diagnostic...

Description Author(s) See Also


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.


Maintainer: Derek Chiu [email protected]


See Also

Useful links:

AlineTalhouk/splendid documentation built on June 7, 2019, 5:23 p.m.