Machine Learning Evaluation Metrics
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appveyor.yml Version 1.1.0 Update May 1, 2016

README.md

MLmetrics

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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")