# mlr3measures v0.3.1

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## Performance Measures for 'mlr3'

Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are.

# mlr3measures

Package website: release | dev

Implements multiple performance measures for supervised learning. Includes over 40 measures for regression and classification. Additionally, meta information about the performance measures can be queried, e.g. what the best and worst possible performances scores are. Internally, checkmate is used to check arguments efficiently - no other runtime dependencies.

The function reference gives an encompassing overview over implemented measures.

Note that explicitly loading this package is not required if you want to use any of these measures in mlr3. Also note that we advise against attaching the package via library() to avoid namespace clashes. Instead, load the namespace via requireNamespace() and use the :: operator.

## Functions in mlr3measures

 Name Description bias Bias binary_params Binary Classification Parameters ce Classification Error bacc Balanced Accuracy bbrier Binary Brier Score auc Area Under the ROC Curve confusion_matrix Calculate Binary Confusion Matrix dor Diagnostic Odds Ratio acc Classification Accuracy classif_params Classification Parameters fpr False Positive Rate fn False Negatives ktau Kendall's tau maxae Max Absolute Error maxse Max Squared Error mbrier Multiclass Brier Score logloss Log Loss ppv Positive Predictive Value mae Mean Absolute Errors mcc Matthews Correlation Coefficient prauc Area Under the Precision-Recall Curve mse Mean Squared Error fdr False Discovery Rate fbeta F-beta Score medse Median Squared Error smape Symmetric Mean Absolute Percent Error fomr False Omission Rate mlr3measures-package mlr3measures: Performance Measures for 'mlr3' mape Mean Absolute Percent Error mauc_aunu Multiclass AUC Scores fp False Positives tnr True Negative Rate pbias Percent Bias tp True Positives npv Negative Predictive Value rmsle Root Mean Squared Log Error rmse Root Mean Squared Error rae Relative Absolute Error sse Sum of Squared Errors tn True Negatives regr_params Regression Parameters rrse Root Relative Squared Error rse Relative Squared Error msle Mean Squared Log Error tpr True Positive Rate measures Measure Registry rsq R Squared medae Median Absolute Errors fnr False Negative Rate sae Sum of Absolute Errors srho Spearman's rho No Results!