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:
easy_install ml_metrics
install.packages("Metrics")
from the R promptcabal install Metrics
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) | ✓ | ✓ | ✓ | ✓ |
(Nonexhaustive and to be added in the future)