MLmetrics v1.1.1


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Machine Learning Evaluation Metrics

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

Functions in MLmetrics

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
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

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