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MLmetrics (version 1.0.0)

Machine Learning Evaluation Metrics

Description

A collection of evaluation metrics, including loss, score and utility functions, that measure regression and classification performance.

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Version

Install

install.packages('MLmetrics')

Monthly Downloads

16,104

Version

1.0.0

License

GPL-2

Issues

9

Pull Requests

1

Stars

69

Forks

15

Maintainer

Yachen Yan

Last Published

May 2nd, 2015

Functions in MLmetrics (1.0.0)

MAE

Mean Absolute Error Loss
ConfusionMatrix

Confusion Matrix
MSE

Mean Square Error Loss
ConfusionDF

Confusion Matrix (Data Frame Format)
RMSE

Root Mean Square Error Loss
ZeroOneLoss

Normalized Zero-One Loss (Classification Error Loss)
RAE

Relative Absolute Error Loss
R2_score

R-Squared (Coefficient of Determination) Regression Score
Recall

Recall
Accuracy

Accuracy
FBeta_Score

F-Beta Score
RRSE

Root Relative Squared Error Loss
Gini

Gini Coefficient
RMSLE

Root Mean Squared Logarithmic Error Loss
Precision

Precision
F1_Score

F1 Score
MedianAE

Median Absolute Error Loss
MLmetrics

MLmetrics: Machine Learning Evaluation Metrics
MultiLogLoss

Multi Class Log Loss
AUC

Area Under the Curve (AUC)
LogLoss

Log loss/Cross-Entropy Loss