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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

0.3.1

License

LGPL-3

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Maintainer

Michel Lang

Last Published

January 6th, 2021

Functions in mlr3measures (0.3.1)

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