RDocumentation
Moon
Learn R
Search all packages and functions
⚠️
There's a newer version (1.1.3) of this package.
Take me there.
MLmetrics (version 1.1.0)
Machine Learning Evaluation Metrics
Description
A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.
Copy Link
Copy
Link to current version
Version
Version
1.1.3
1.1.1
1.1.0
1.0.0
Down Chevron
Install
install.packages('MLmetrics')
Monthly Downloads
9,710
Version
1.1.0
License
GPL-2
Issues
8
Pull Requests
1
Stars
67
Forks
15
Repository
http://github.com/yanyachen/MLmetrics
Maintainer
Yachen Yan
Last Published
May 1st, 2016
Functions in MLmetrics (1.1.0)
Search functions
Area_Under_Curve
Calculate the Area Under the Curve
NormalizedGini
Normalized Gini Coefficient
FBeta_Score
F-Beta Score
GainAUC
Area Under the Gain Chart
Possion_LogLoss
Possion Log loss
MAPE
Mean Absolute Percentage Error Loss
ConfusionMatrix
Confusion Matrix
RMSLE
Root Mean Squared Logarithmic Error Loss
KS_Stat
Kolmogorov-Smirnov Statistic
R2_Score
R-Squared (Coefficient of Determination) Regression Score
LiftAUC
Area Under the Lift Chart
MultiLogLoss
Multi Class Log Loss
MSE
Mean Square Error Loss
RMSE
Root Mean Square Error Loss
Sensitivity
Sensitivity
RMSPE
Root Mean Square Percentage Error Loss
Precision
Precision
Specificity
Specificity
RAE
Relative Absolute Error Loss
RRSE
Root Relative Squared Error Loss
PRAUC
Area Under the Precision-Recall Curve (PR AUC)
Gini
Gini Coefficient
Recall
Recall
MedianAPE
Median Absolute Percentage Error Loss
MAE
Mean Absolute Error Loss
ZeroOneLoss
Normalized Zero-One Loss (Classification Error Loss)
AUC
Area Under the Receiver Operating Characteristic Curve (ROC AUC)
F1_Score
F1 Score
ConfusionDF
Confusion Matrix (Data Frame Format)
LogLoss
Log loss / Cross-Entropy Loss
Accuracy
Accuracy
MLmetrics
MLmetrics: Machine Learning Evaluation Metrics
MedianAE
Median Absolute Error Loss