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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

Install

install.packages('MLmetrics')

Monthly Downloads

20,926

Version

1.1.1

License

GPL-2

Issues

9

Pull Requests

1

Stars

69

Forks

15

Maintainer

Yachen Yan

Last Published

May 13th, 2016

Functions in MLmetrics (1.1.1)

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