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GitHub - benhamner/Metrics: Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave
Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave - benhamner/Metrics
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GitHub - benhamner/Metrics: Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

GitHub - benhamner/Metrics: Machine learning evaluation metrics, implemented in Python, R, Haskell, and MATLAB / Octave

Note: the current releases of this toolbox are a beta release, to test working with Haskell's, Python's, and R's code repositories.

Build Status

Metrics provides implementations of various supervised machine learning evaluation metrics in the following languages:

  • Python easy_install ml_metrics
  • R install.packages("Metrics") from the R prompt
  • Haskell cabal install Metrics
  • MATLAB / Octave (clone the repo & run setup from the MATLAB command line)

For more detailed installation instructions, see the README for each implementation.

EVALUATION METRICS

TO IMPLEMENT

  • F1 score
  • Multiclass log loss
  • Lift
  • Average Precision for binary classification
  • precision / recall break-even point
  • cross-entropy
  • True Pos / False Pos / True Neg / False Neg rates
  • precision / recall / sensitivity / specificity
  • mutual information

HIGHER LEVEL TRANSFORMATIONS TO HANDLE

  • GroupBy / Reduce
  • Weight individual samples or groups

PROPERTIES METRICS CAN HAVE

(Nonexhaustive and to be added in the future)

  • Min or Max (optimize through minimization or maximization)

  • Binary Classification

    • Scores predicted class labels
    • Scores predicted ranking (most likely to least likely for being in one class)
    • Scores predicted probabilities
  • Multiclass Classification

    • Scores predicted class labels
    • Scores predicted probabilities
  • Regression

  • Discrete Rater Comparison (confusion matrix)

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