MLmetrics v1.1.1

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by Yachen Yan View Source

Machine Learning Evaluation Metrics

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

Name	Description
AUC	Area Under the Receiver Operating Characteristic Curve (ROC AUC)
LiftAUC	Area Under the Lift Chart
MLmetrics	MLmetrics: Machine Learning Evaluation Metrics
RAE	Relative Absolute Error Loss
MAPE	Mean Absolute Percentage Error Loss
ConfusionDF	Confusion Matrix (Data Frame Format)
MultiLogLoss	Multi Class Log Loss
Poisson_LogLoss	Poisson Log loss
F1_Score	F1 Score
NormalizedGini	Normalized Gini Coefficient
KS_Stat	Kolmogorov-Smirnov Statistic
MedianAE	Median Absolute Error Loss
RMSE	Root Mean Square Error Loss
R2_Score	R-Squared (Coefficient of Determination) Regression Score
ZeroOneLoss	Normalized Zero-One Loss (Classification Error Loss)
MSE	Mean Square Error Loss
LogLoss	Log loss / Cross-Entropy Loss
PRAUC	Area Under the Precision-Recall Curve (PR AUC)
RMSPE	Root Mean Square Percentage Error Loss
RMSLE	Root Mean Squared Logarithmic Error Loss
Area_Under_Curve	Calculate the Area Under the Curve
Accuracy	Accuracy
FBeta_Score	F-Beta Score
Recall	Recall
ConfusionMatrix	Confusion Matrix
RRSE	Root Relative Squared Error Loss
Precision	Precision
MedianAPE	Median Absolute Percentage Error Loss
Specificity	Specificity
Gini	Gini Coefficient
GainAUC	Area Under the Gain Chart
MAE	Mean Absolute Error Loss
Sensitivity	Sensitivity
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Type	Package
URL	http://github.com/yanyachen/MLmetrics
BugReports	http://github.com/yanyachen/MLmetrics/issues
License	GPL-2
LazyData	true
RoxygenNote	5.0.1
NeedsCompilation	no
Packaged	2016-05-09 06:13:55 UTC; Administrator
Repository	CRAN
Date/Publication	2016-05-13 23:57:26