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mlr3measures

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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.

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Install

install.packages('mlr3measures')

Monthly Downloads

17,897

Version

1.2.0

License

LGPL-3

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Maintainer

Marc Becker

Last Published

November 25th, 2025

Functions in mlr3measures (1.2.0)

fomr

False Omission Rate
fbeta

F-beta Score
gmean

Geometric Mean of Recall and Specificity
fn

False Negatives
fpr

False Positive Rate
fnr

False Negative Rate
confusion_matrix

Calculate Binary Confusion Matrix
dor

Diagnostic Odds Ratio
gpr

Geometric Mean of Precision and Recall
logloss

Log Loss
jaccard

Jaccard Similarity Index
mae

Mean Absolute Error
fp

False Positives
ktau

Kendall's tau
linex

Linear-Exponential Loss (per observation)
mape

Mean Absolute Percent Error
fdr

False Discovery Rate
mauc_aunu

Multiclass AUC Scores
maxae

Max Absolute Error
maxse

Max Squared Error
npv

Negative Predictive Value
obs_logloss

Observation-wise Log Loss
medae

Median Absolute Error
mbrier

Multiclass Brier Score
mse

Mean Squared Error
measures

Measure Registry
medse

Median Squared Error
mlr3measures-package

mlr3measures: Performance Measures for 'mlr3'
rmse

Root Mean Squared Error
regr_params

Regression Parameters
mcc

Matthews Correlation Coefficient
rrse

Root Relative Squared Error
rmsle

Root Mean Squared Log Error
pinball

Average Pinball Loss
se_binary

Binary Squared Error
ppv

Positive Predictive Value
pbias

Percent Bias
phi

Phi Coefficient Similarity
msle

Mean Squared Log Error
similarity_params

Similarity Parameters
srho

Spearman's rho
se

Squared Error (per observation)
sae

Sum of Absolute Errors
tnr

True Negative Rate
tn

True Negatives
sle

Squared Log Error (per observation)
rae

Relative Absolute Error
prauc

Area Under the Precision-Recall Curve
rse

Relative Squared Error
rsq

R Squared
smape

Symmetric Mean Absolute Percent Error
tp

True Positives
tpr

True Positive Rate
zero_one

Zero-One Classification Loss (per observation)
sse

Sum of Squared Errors
binary_params

Binary Classification Parameters
bbrier

Binary Brier Score
ae

Absolute Error (per observation)
acc

Classification Accuracy
bacc

Balanced Accuracy
auc

Area Under the ROC Curve
bias

Bias
ape

Absolute Percentage Error (per observation)
ce

Classification Error
classif_params

Classification Parameters