Linux/Mac: Windows:

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
  Precision (micro averaged)
  Precision (macro averaged)
  Recall
  Recall (micro averaged)
  Recall (macro averaged)
  Sensitivity
  Specificity
  F1 Score
  F1 Score (micro averaged)
  F1 Score (macro averaged)
  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")