Package index • mlr3measures

Binary Classification Measures

auc(): Area Under the ROC Curve

bbrier(): Binary Brier Score

dor(): Diagnostic Odds Ratio

fbeta(): F-beta Score

fdr(): False Discovery Rate

fn(): False Negatives

fnr(): False Negative Rate

fomr(): False Omission Rate

fp(): False Positives

fpr(): False Positive Rate

gmean(): Geometric Mean of Recall and Specificity

gpr(): Geometric Mean of Precision and Recall

npv(): Negative Predictive Value

ppv() precision(): Positive Predictive Value

prauc(): Area Under the Precision-Recall Curve

tn(): True Negatives

tnr() specificity(): True Negative Rate

tp(): True Positives

tpr() recall() sensitivity(): True Positive Rate

Classification Measures

acc(): Classification Accuracy

bacc(): Balanced Accuracy

ce(): Classification Error

logloss(): Log Loss

mauc_aunu() mauc_aunp() mauc_au1u() mauc_au1p() mauc_mu(): Multiclass AUC Scores

mbrier(): Multiclass Brier Score

mcc(): Matthews Correlation Coefficient

zero_one() one_zero(): Zero-One Classification Loss (per observation)

Regression Measures

ae(): Absolute Error (per observation)

ape(): Absolute Percentage Error (per observation)

bias(): Bias

ktau(): Kendall's tau

linex(): Linear-Exponential Loss (per observation)

mae(): Mean Absolute Error

mape(): Mean Absolute Percent Error

maxae(): Max Absolute Error

maxse(): Max Squared Error

medae(): Median Absolute Error

medse(): Median Squared Error

mse(): Mean Squared Error

msle(): Mean Squared Log Error

pbias(): Percent Bias

pinball(): Average Pinball Loss

rae(): Relative Absolute Error

rmse(): Root Mean Squared Error

rmsle(): Root Mean Squared Log Error

rrse(): Root Relative Squared Error

rse(): Relative Squared Error

rsq(): R Squared

sae(): Sum of Absolute Errors

se(): Squared Error (per observation)

sle(): Squared Log Error (per observation)

smape(): Symmetric Mean Absolute Percent Error

srho(): Spearman's rho

sse(): Sum of Squared Errors

Similarity Measures

jaccard(): Jaccard Similarity Index

phi(): Phi Coefficient Similarity

Misc

measures: Measure Registry

confusion_matrix(): Calculate Binary Confusion Matrix

mlr3measures mlr3measures-package: mlr3measures: Performance Measures for 'mlr3'