Machine Learning Evaluation Metrics
A collection of evaluation metrics, including loss, score and utility functions, that measure regression, classification and ranking performance.
- Regression:
Mean Squared Error
Root Mean Squared Error
Root Mean Squared Logarithmic Error
Root Mean Square Percentage Error
Root Relative Squared Error
Mean Absolute Error
Mean Absolute Percentage Error
Median Absolute Error
Median Absolute Percentage Error
Relative Absolute Error
R-Squared (Coefficient of Determination) Regression Score
Poisson LogLoss
Normalized Gini Coefficient - Classification:
Confusion Matrix
Zero-One Loss
Accuracy
Precision
Recall
Sensitivity
Specificity
F1 Score
F-Beta Score
Log loss / Cross-Entropy Loss
Multi Class Log Loss
AUC
Gini
PRAUC
LiftAUC
GainAUC
Kolmogorov-Smirnov Statistic
To install:
- the stable version from CRAN:
install.packages("MLmetrics")- the latest development version:
devtools::install_github("yanyachen/MLmetrics")