Performance Measures for 'mlr3'

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Documentation for package ‘mlr3measures’ version 0.4.1

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mlr3measures-package mlr3measures: Performance Measures for 'mlr3'
acc Classification Accuracy
auc Area Under the ROC Curve
bacc Balanced Accuracy
bbrier Binary Brier Score
bias Bias
ce Classification Error
confusion_matrix Calculate Binary Confusion Matrix
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
jaccard Jaccard Similarity Index
ktau Kendall's tau
logloss Log Loss
mae Mean Absolute Error
mape Mean Absolute Percent Error
mauc_au1p Multiclass AUC Scores
mauc_au1u Multiclass AUC Scores
mauc_aunp Multiclass AUC Scores
mauc_aunu Multiclass AUC Scores
maxae Max Absolute Error
maxse Max Squared Error
mbrier Multiclass Brier Score
mcc Matthews Correlation Coefficient
measures Measure Registry
medae Median Absolute Error
medse Median Squared Error
mlr3measures mlr3measures: Performance Measures for 'mlr3'
mse Mean Squared Error
msle Mean Squared Log Error
npv Negative Predictive Value
pbias Percent Bias
phi Phi Coefficient Similarity
ppv Positive Predictive Value
prauc Area Under the Precision-Recall Curve
precision Positive Predictive Value
rae Relative Absolute Error
recall True Positive Rate
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
sensitivity True Positive Rate
smape Symmetric Mean Absolute Percent Error
specificity True Negative Rate
srho Spearman's rho
sse Sum of Squared Errors
tn True Negatives
tnr True Negative Rate
tp True Positives
tpr True Positive Rate