Package: MLmetrics 1.1.3

MLmetrics: Machine Learning Evaluation Metrics

A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.

Authors:Yachen Yan [aut, cre]

# Install 'MLmetrics' in R:

install.packages('MLmetrics', repos = c('https://yanyachen.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

Last updated from:91eb670427. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	NOTE	94
source / vignettes	OK	133
linux-release	NOTE	93
macos-release	NOTE	138
macos-oldrel	NOTE	157
windows-devel	NOTE	57
windows-release	NOTE	58
windows-oldrel	NOTE	47
wasm-release	OK	77

Help page	Topics
Accuracy	Accuracy
Calculate the Area Under the Curve	Area_Under_Curve
Area Under the Receiver Operating Characteristic Curve (ROC AUC)	AUC
Confusion Matrix	ConfusionMatrix
F1 Score	F1_Score
F-Beta Score	FBeta_Score
Area Under the Gain Chart	GainAUC
Gini Coefficient	Gini
Kolmogorov-Smirnov Statistic	KS_Stat
Area Under the Lift Chart	LiftAUC
Log loss / Cross-Entropy Loss	LogLoss
Mean Absolute Error Loss	MAE
Mean Absolute Percentage Error Loss	MAPE
Median Absolute Error Loss	MedianAE
Median Absolute Percentage Error Loss	MedianAPE
MLmetrics: Machine Learning Evaluation Metrics	MLmetrics-package MLmetrics
Mean Square Error Loss	MSE
Multi Class Log Loss	MultiLogLoss
Normalized Gini Coefficient	NormalizedGini
Poisson Log loss	Poisson_LogLoss
Area Under the Precision-Recall Curve (PR AUC)	PRAUC
Precision	Precision
R-Squared (Coefficient of Determination) Regression Score	R2_Score
Relative Absolute Error Loss	RAE
Recall	Recall
Root Mean Square Error Loss	RMSE
Root Mean Squared Logarithmic Error Loss	RMSLE
Root Mean Square Percentage Error Loss	RMSPE
Root Relative Squared Error Loss	RRSE
Sensitivity	Sensitivity
Specificity	Specificity
Normalized Zero-One Loss (Classification Error Loss)	ZeroOneLoss