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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.

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Version

Install

install.packages('MLmetrics')

Monthly Downloads

16,104

Version

1.1.0

License

GPL-2

Issues

9

Pull Requests

1

Stars

69

Forks

15

Maintainer

Yachen Yan

Last Published

May 1st, 2016

Functions in MLmetrics (1.1.0)

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