Machine Learning Evaluation Metrics

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Documentation for package ‘MLmetrics’ version 1.1.3

• DESCRIPTION file.

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