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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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Version

Install

install.packages('MLmetrics')

Monthly Downloads

14,501

Version

1.1.3

License

GPL-2

Issues

8

Pull Requests

1

Stars

69

Forks

14

Maintainer

Yachen Yan

Last Published

April 13th, 2024

Functions in MLmetrics (1.1.3)

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