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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

0.4.0

License

LGPL-3

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Maintainer

Michel Lang

Last Published

September 26th, 2021

Functions in mlr3measures (0.4.0)

bacc

Balanced Accuracy
confusion_matrix

Calculate Binary Confusion Matrix
ce

Classification Error
bias

Bias
acc

Classification Accuracy
bbrier

Binary Brier Score
dor

Diagnostic Odds Ratio
auc

Area Under the ROC Curve
binary_params

Binary Classification Parameters
classif_params

Classification Parameters
fbeta

F-beta Score
measures

Measure Registry
mcc

Matthews Correlation Coefficient
rae

Relative Absolute Error
regr_params

Regression Parameters
fdr

False Discovery Rate
fpr

False Positive Rate
mlr3measures-package

mlr3measures: Performance Measures for 'mlr3'
mae

Mean Absolute Error
jaccard

Jaccard Similarity Index
mape

Mean Absolute Percent Error
mse

Mean Squared Error
rrse

Root Relative Squared Error
rmse

Root Mean Squared Error
rsq

R Squared
rmsle

Root Mean Squared Log Error
fomr

False Omission Rate
ktau

Kendall's tau
logloss

Log Loss
fp

False Positives
maxae

Max Absolute Error
mauc_aunu

Multiclass AUC Scores
npv

Negative Predictive Value
similarity_params

Similarity Parameters
msle

Mean Squared Log Error
rse

Relative Squared Error
fn

False Negatives
smape

Symmetric Mean Absolute Percent Error
tnr

True Negative Rate
tn

True Negatives
fnr

False Negative Rate
maxse

Max Squared Error
sae

Sum of Absolute Errors
tpr

True Positive Rate
tp

True Positives
medae

Median Absolute Error
pbias

Percent Bias
mbrier

Multiclass Brier Score
phi

Phi Coefficient Similarity
srho

Spearman's rho
sse

Sum of Squared Errors
medse

Median Squared Error
ppv

Positive Predictive Value
prauc

Area Under the Precision-Recall Curve