Learn R Programming

⚠️There's a newer version (1.0.0) of this package.Take me there.

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.

Copy Link

Version

Install

install.packages('mlr3measures')

Monthly Downloads

7,591

Version

0.5.0

License

LGPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Michel Lang

Last Published

August 5th, 2022

Functions in mlr3measures (0.5.0)

ae

Absolute Error (per observation)
ce

Classification Error
ape

Absolute Percentage Error (per observation)
binary_params

Binary Classification Parameters
auc

Area Under the ROC Curve
bias

Bias
acc

Classification Accuracy
bacc

Balanced Accuracy
classif_params

Classification Parameters
bbrier

Binary Brier Score
confusion_matrix

Calculate Binary Confusion Matrix
jaccard

Jaccard Similarity Index
fpr

False Positive Rate
fn

False Negatives
fnr

False Negative Rate
fp

False Positives
fomr

False Omission Rate
mae

Mean Absolute Error
mape

Mean Absolute Percent Error
maxse

Max Squared Error
mbrier

Multiclass Brier Score
dor

Diagnostic Odds Ratio
fdr

False Discovery Rate
measures

Measure Registry
mcc

Matthews Correlation Coefficient
fbeta

F-beta Score
logloss

Log Loss
ktau

Kendall's tau
maxae

Max Absolute Error
mauc_aunu

Multiclass AUC Scores
pbias

Percent Bias
phi

Phi Coefficient Similarity
npv

Negative Predictive Value
msle

Mean Squared Log Error
ppv

Positive Predictive Value
rae

Relative Absolute Error
prauc

Area Under the Precision-Recall Curve
regr_params

Regression Parameters
rse

Relative Squared Error
rrse

Root Relative Squared Error
mlr3measures-package

mlr3measures: Performance Measures for 'mlr3'
rmse

Root Mean Squared Error
mse

Mean Squared Error
rmsle

Root Mean Squared Log Error
sae

Sum of Absolute Errors
rsq

R Squared
sle

Squared Log Error (per observation)
smape

Symmetric Mean Absolute Percent Error
medae

Median Absolute Error
srho

Spearman's rho
medse

Median Squared Error
tn

True Negatives
tnr

True Negative Rate
similarity_params

Similarity Parameters
tpr

True Positive Rate
se

Squared Error (per observation)
tp

True Positives
zero_one

Zero-One Classification Loss (per observation)
sse

Sum of Squared Errors