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Straightforward and detailed evaluation of machine learning models. 'MLeval' can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. 'MLeval' accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. 'MLeval' produces a range of evaluation metrics with confidence intervals.
| Version: | 0.3 |
| Depends: | R (≥ 3.5.0) |
| Imports: | ggplot2 |
| Suggests: | knitr, rmarkdown |
| Published: | 2020-02-12 |
| Author: | Christopher R John |
| Maintainer: | Christopher R John <chris.r.john86 at gmail.com> |
| License: | AGPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | MLeval results |
| Reference manual: | MLeval.pdf |
| Vignettes: |
MLeval |
| Package source: | MLeval_0.3.tar.gz |
| Windows binaries: | r-devel: MLeval_0.3.zip, r-release: MLeval_0.3.zip, r-oldrel: MLeval_0.3.zip |
| macOS binaries: | r-release (arm64): MLeval_0.3.tgz, r-oldrel (arm64): MLeval_0.3.tgz, r-release (x86_64): MLeval_0.3.tgz, r-oldrel (x86_64): MLeval_0.3.tgz |
| Old sources: | MLeval archive |
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These binaries (installable software) and packages are in development.
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