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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

0.6.0

License

LGPL-3

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Maintainer

Marc Becker

Last Published

July 21st, 2024

Functions in mlr3measures (0.6.0)

fpr

False Positive Rate
gmean

Geometric Mean of Recall and Specificity
mlr3measures-package

mlr3measures: Performance Measures for 'mlr3'
dor

Diagnostic Odds Ratio
mse

Mean Squared Error
maxse

Max Squared Error
fdr

False Discovery Rate
mbrier

Multiclass Brier Score
maxae

Max Absolute Error
fbeta

F-beta Score
mauc_aunu

Multiclass AUC Scores
ktau

Kendall's tau
rsq

R Squared
sae

Sum of Absolute Errors
tn

True Negatives
mape

Mean Absolute Percent Error
mae

Mean Absolute Error
logloss

Log Loss
msle

Mean Squared Log Error
npv

Negative Predictive Value
tnr

True Negative Rate
gpr

Geometric Mean of Precision and Recall
jaccard

Jaccard Similarity Index
mcc

Matthews Correlation Coefficient
measures

Measure Registry
srho

Spearman's rho
sse

Sum of Squared Errors
se

Squared Error (per observation)
rae

Relative Absolute Error
similarity_params

Similarity Parameters
medae

Median Absolute Error
phi

Phi Coefficient Similarity
regr_params

Regression Parameters
pbias

Percent Bias
zero_one

Zero-One Classification Loss (per observation)
ppv

Positive Predictive Value
prauc

Area Under the Precision-Recall Curve
tpr

True Positive Rate
tp

True Positives
rmse

Root Mean Squared Error
medse

Median Squared Error
rmsle

Root Mean Squared Log Error
sle

Squared Log Error (per observation)
smape

Symmetric Mean Absolute Percent Error
rse

Relative Squared Error
rrse

Root Relative Squared Error
binary_params

Binary Classification Parameters
acc

Classification Accuracy
auc

Area Under the ROC Curve
ce

Classification Error
bacc

Balanced Accuracy
ae

Absolute Error (per observation)
bbrier

Binary Brier Score
bias

Bias
ape

Absolute Percentage Error (per observation)
fnr

False Negative Rate
fn

False Negatives
classif_params

Classification Parameters
confusion_matrix

Calculate Binary Confusion Matrix
fp

False Positives
fomr

False Omission Rate