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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.

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Install

install.packages('mlr3measures')

Monthly Downloads

7,591

Version

0.4.1

License

LGPL-3

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Maintainer

Michel Lang

Last Published

January 13th, 2022

Functions in mlr3measures (0.4.1)

dor

Diagnostic Odds Ratio
auc

Area Under the ROC Curve
acc

Classification Accuracy
ce

Classification Error
bacc

Balanced Accuracy
bbrier

Binary Brier Score
confusion_matrix

Calculate Binary Confusion Matrix
bias

Bias
classif_params

Classification Parameters
binary_params

Binary Classification Parameters
fomr

False Omission Rate
fbeta

F-beta Score
mae

Mean Absolute Error
mlr3measures-package

mlr3measures: Performance Measures for 'mlr3'
fp

False Positives
mape

Mean Absolute Percent Error
mse

Mean Squared Error
logloss

Log Loss
mbrier

Multiclass Brier Score
maxse

Max Squared Error
ktau

Kendall's tau
pbias

Percent Bias
phi

Phi Coefficient Similarity
fdr

False Discovery Rate
mcc

Matthews Correlation Coefficient
measures

Measure Registry
srho

Spearman's rho
fnr

False Negative Rate
maxae

Max Absolute Error
mauc_aunu

Multiclass AUC Scores
msle

Mean Squared Log Error
fn

False Negatives
npv

Negative Predictive Value
sse

Sum of Squared Errors
medse

Median Squared Error
rae

Relative Absolute Error
medae

Median Absolute Error
fpr

False Positive Rate
rsq

R Squared
ppv

Positive Predictive Value
sae

Sum of Absolute Errors
regr_params

Regression Parameters
tnr

True Negative Rate
tn

True Negatives
rrse

Root Relative Squared Error
rse

Relative Squared Error
tp

True Positives
tpr

True Positive Rate
prauc

Area Under the Precision-Recall Curve
similarity_params

Similarity Parameters
smape

Symmetric Mean Absolute Percent Error
jaccard

Jaccard Similarity Index
rmse

Root Mean Squared Error
rmsle

Root Mean Squared Log Error