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Showing content from https://github.com/benhamner/Metrics below:

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

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

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 Metric Python R Haskell MATLAB / Octave Absolute Error (AE) ✓ ✓ ✓ ✓ Average Precision at K (APK, AP@K) ✓ ✓ ✓ ✓ Area Under the ROC (AUC) ✓ ✓ ✓ ✓ Classification Error (CE) ✓ ✓ ✓ ✓ F1 Score (F1) ✓ Gini ✓ Levenshtein ✓ ✓ ✓ Log Loss (LL) ✓ ✓ ✓ ✓ Mean Log Loss (LogLoss) ✓ ✓ ✓ ✓ Mean Absolute Error (MAE) ✓ ✓ ✓ ✓ Mean Average Precision at K (MAPK, MAP@K) ✓ ✓ ✓ ✓ Mean Quadratic Weighted Kappa ✓ ✓ ✓ Mean Squared Error (MSE) ✓ ✓ ✓ ✓ Mean Squared Log Error (MSLE) ✓ ✓ ✓ ✓ Normalized Gini ✓ Quadratic Weighted Kappa ✓ ✓ ✓ Relative Absolute Error (RAE) ✓ Root Mean Squared Error (RMSE) ✓ ✓ ✓ ✓ Relative Squared Error (RSE) ✓ Root Relative Squared Error (RRSE) ✓ Root Mean Squared Log Error (RMSLE) ✓ ✓ ✓ ✓ Squared Error (SE) ✓ ✓ ✓ ✓ Squared Log Error (SLE) ✓ ✓ ✓ ✓

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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