Package that provides the biggest amount of statistical measures in the whole R world!
Includes measures of regression, (multiclass) classification, clustering, survival and multilabel classification.
It is based on measures of mlr.
The development version
devtools::install_github("mlr-org/measures")
The available measures can be looked up by
listAllMeasures()
| function_name | description | task |
|---|---|---|
| SSE | Sum of squared errors | regression |
| MSE | Mean of squared errors | regression |
| RMSE | Root mean squared error | regression |
| MEDSE | Median of squared errors | regression |
| SAE | Sum of absolute errors | regression |
| MAE | Mean of absolute errors | regression |
| MEDAE | Median of absolute errors | regression |
| RSQ | Coefficient of determination | regression |
| EXPVAR | Explained variance | regression |
| ARSQ | Adjusted coefficient of determination | regression |
| RRSE | Root relative squared error | regression |
| RAE | Relative absolute error | regression |
| MAPE | Mean absolute percentage error | regression |
| MSLE | Mean squared logarithmic error | regression |
| RMSLE | Root mean squared logarithmic error | regression |
| KendallTau | Kendall's tau | regression |
| SpearmanRho | Spearman's rho | regression |
| AUC | Area under the curve | binary classification |
| Brier | Brier score | binary classification |
| BrierScaled | Brier scaled | binary classification |
| BAC | Balanced accuracy | binary classification |
| TP | True positives | binary classification |
| TN | True negatives | binary classification |
| FP | False positives | binary classification |
| FN | False negatives | binary classification |
| TPR | True positive rate | binary classification |
| TNR | True negative rate | binary classification |
| FPR | False positive rate | binary classification |
| FNR | False negative rate | binary classification |
| PPV | Positive predictive value | binary classification |
| NPV | Negative predictive value | binary classification |
| FDR | False discovery rate | binary classification |
| MCC | Matthews correlation coefficient | binary classification |
| F1 | F1 measure | binary classification |
| GMEAN | G-mean | binary classification |
| GPR | Geometric mean of precision and recall. | binary classification |
| MMCE | Mean misclassification error | multiclass classification |
| ACC | Accuracy | multiclass classification |
| BER | Balanced error rate | multiclass classification |
| multiclass.AUNU | Average 1 vs. rest multiclass AUC | multiclass classification |
| multiclass.AUNP | Weighted average 1 vs. rest multiclass AUC | multiclass classification |
| multiclass.AU1U | Average 1 vs. 1 multiclass AUC | multiclass classification |
| multiclass.AU1P | Weighted average 1 vs. 1 multiclass AUC | multiclass classification |
| multiclass.Brier | Multiclass Brier score | multiclass classification |
| Logloss | Logarithmic loss | multiclass classification |
| SSR | Spherical Scoring Rule | multiclass classification |
| QSR | Quadratic Scoring Rule | multiclass classification |
| LSR | Logarithmic Scoring Rule | multiclass classification |
| KAPPA | Cohen's kappa | multiclass classification |
| WKAPPA | Mean quadratic weighted kappa | multiclass classification |
| MultilabelHamloss | Hamming loss | multilabel |
| MultilabelSubset01 | Subset-0-1 loss | multilabel |
| MultilabelF1 | F1 measure (multilabel) | multilabel |
| MultilabelACC | Accuracy (multilabel) | multilabel |
| MultilabelPPV | Positive predictive value (multilabel) | multilabel |
| MultilabelTPR | TPR (multilabel) | multilabel |