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MLmetrics (version 1.1.3)
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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1.1.3
1.1.1
1.1.0
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Install
install.packages('MLmetrics')
Monthly Downloads
9,710
Version
1.1.3
License
GPL-2
Issues
8
Pull Requests
1
Stars
67
Forks
15
Repository
https://github.com/yanyachen/MLmetrics
Maintainer
Yachen Yan
Last Published
April 13th, 2024
Functions in MLmetrics (1.1.3)
Search functions
Accuracy
Accuracy
Precision
Precision
LogLoss
Log loss / Cross-Entropy Loss
RAE
Relative Absolute Error Loss
RMSE
Root Mean Square Error Loss
RMSPE
Root Mean Square Percentage Error Loss
ZeroOneLoss
Normalized Zero-One Loss (Classification Error Loss)
R2_Score
R-Squared (Coefficient of Determination) Regression Score
Poisson_LogLoss
Poisson Log loss
MultiLogLoss
Multi Class Log Loss
NormalizedGini
Normalized Gini Coefficient
PRAUC
Area Under the Precision-Recall Curve (PR AUC)
Sensitivity
Sensitivity
Specificity
Specificity
RRSE
Root Relative Squared Error Loss
Recall
Recall
FBeta_Score
F-Beta Score
ConfusionMatrix
Confusion Matrix
F1_Score
F1 Score
AUC
Area Under the Receiver Operating Characteristic Curve (ROC AUC)
Area_Under_Curve
Calculate the Area Under the Curve
Gini
Gini Coefficient
KS_Stat
Kolmogorov-Smirnov Statistic
GainAUC
Area Under the Gain Chart
ConfusionDF
Confusion Matrix (Data Frame Format)
MAPE
Mean Absolute Percentage Error Loss
MedianAE
Median Absolute Error Loss
MLmetrics
MLmetrics: Machine Learning Evaluation Metrics
LiftAUC
Area Under the Lift Chart
MSE
Mean Square Error Loss
MAE
Mean Absolute Error Loss
MedianAPE
Median Absolute Percentage Error Loss
RMSLE
Root Mean Squared Logarithmic Error Loss