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MLmetrics (version 1.1.1)
Machine Learning Evaluation Metrics
Description
A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.
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Version
1.1.1
1.1.0
1.0.0
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Install
install.packages('MLmetrics')
Monthly Downloads
12,888
Version
1.1.1
License
GPL-2
Issues
8
Pull Requests
1
Stars
65
Forks
15
Repository
http://github.com/yanyachen/MLmetrics
Maintainer
Yachen Yan
Last Published
May 13th, 2016
Functions in MLmetrics (1.1.1)
Search functions
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