Metrics alternatives and similar packages
Based on the "Machine Learning" category.
Alternatively, view Metrics alternatives based on common mentions on social networks and blogs.
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SciKit-Learn Laboratory
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README
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 METRICS
Evaluation MetricPythonRHaskellMATLAB / 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)✓✓✓✓
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)