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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

7,709

Version

0.3.0

License

LGPL-3

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Maintainer

Michel Lang

Last Published

October 5th, 2020

Functions in mlr3measures (0.3.0)

classif_params

Classification Parameters
acc

Classification Accuracy
auc

Area Under the ROC Curve
bacc

Balanced Accuracy
binary_params

Binary Classification Parameters
bias

Bias
dor

Diagnostic Odds Ratio
confusion_matrix

Calculate Binary Confusion Matrix
fomr

False Omission Rate
bbrier

Binary Brier Score
prauc

Area Under the Precision-Recall Curve
ktau

Kendall's tau
fpr

False Positive Rate
ppv

Positive Predictive Value
tnr

True Negative Rate
tp

True Positives
fp

False Positives
fdr

False Discovery Rate
mbrier

Multiclass Brier Score
fbeta

F-beta Score
medse

Median Squared Error
mcc

Matthews Correlation Coefficient
mape

Mean Absolute Percent Error
mlr3measures-package

mlr3measures: Performance Measures for 'mlr3'
fn

False Negatives
mauc_aunu

Multiclass AUC Scores
regr_params

Regression Parameters
rae

Relative Absolute Error
mse

Mean Squared Error
msle

Mean Squared Log Error
mae

Mean Absolute Errors
logloss

Log Loss
maxae

Max Absolute Error
measures

Measure Registry
fnr

False Negative Rate
rsq

R Squared
maxse

Max Squared Error
rmse

Root Mean Squared Error
npv

Negative Predictive Value
pbias

Percent Bias
smape

Symmetric Mean Absolute Percent Error
srho

Spearman's rho
sae

Sum of Absolute Errors
tpr

True Positive Rate
sse

Sum of Squared Errors
tn

True Negatives
rmsle

Root Mean Squared Log Error
medae

Median Absolute Errors
rrse

Root Relative Squared Error
rse

Relative Squared Error
ce

Classification Error