MLeval

Machine Learning Model Evaluation

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.

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Description file content

Package
MLeval
Title
Machine Learning Model Evaluation
Version
0.2
Author
Christopher R John
Maintainer
Christopher R John
Description
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.
License
AGPL-3
Encoding
UTF-8
LazyData
true
Depends
R (>= 3.5.0)
Suggests
knitr, rmarkdown
Imports
ggplot2
VignetteBuilder
knitr
RoxygenNote
6.1.1
NeedsCompilation
no
Packaged
2019-11-28 06:58:55 UTC; christopher
Repository
CRAN
Date/Publication
2019-11-30 01:20:13 UTC

install.packages('MLeval')

0.2

15 days ago

Christopher R John

AGPL-3

Depends on

R (>= 3.5.0)

Imports

ggplot2

Suggests

knitr, rmarkdown

Discussions