Learn R Programming

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

8,027

Version

1.0.0

License

LGPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Last Published

September 11th, 2024

Functions in mlr3measures (1.0.0)

maxse

Max Squared Error
gmean

Geometric Mean of Recall and Specificity
linex

Linear-Exponential Loss (per observation)
mauc_aunu

Multiclass AUC Scores
mae

Mean Absolute Error
gpr

Geometric Mean of Precision and Recall
ktau

Kendall's tau
jaccard

Jaccard Similarity Index
mcc

Matthews Correlation Coefficient
measures

Measure Registry
mbrier

Multiclass Brier Score
mse

Mean Squared Error
pbias

Percent Bias
mlr3measures-package

mlr3measures: Performance Measures for 'mlr3'
medse

Median Squared Error
npv

Negative Predictive Value
msle

Mean Squared Log Error
medae

Median Absolute Error
pinball

Average Pinball Loss
phi

Phi Coefficient Similarity
rrse

Root Relative Squared Error
prauc

Area Under the Precision-Recall Curve
rse

Relative Squared Error
rmse

Root Mean Squared Error
rae

Relative Absolute Error
rmsle

Root Mean Squared Log Error
ppv

Positive Predictive Value
regr_params

Regression Parameters
sae

Sum of Absolute Errors
rsq

R Squared
se

Squared Error (per observation)
smape

Symmetric Mean Absolute Percent Error
zero_one

Zero-One Classification Loss (per observation)
srho

Spearman's rho
sle

Squared Log Error (per observation)
tn

True Negatives
tnr

True Negative Rate
sse

Sum of Squared Errors
tp

True Positives
similarity_params

Similarity Parameters
tpr

True Positive Rate
acc

Classification Accuracy
ape

Absolute Percentage Error (per observation)
bacc

Balanced Accuracy
ce

Classification Error
bias

Bias
classif_params

Classification Parameters
ae

Absolute Error (per observation)
auc

Area Under the ROC Curve
bbrier

Binary Brier Score
fn

False Negatives
fnr

False Negative Rate
confusion_matrix

Calculate Binary Confusion Matrix
dor

Diagnostic Odds Ratio
fomr

False Omission Rate
fpr

False Positive Rate
fbeta

F-beta Score
fdr

False Discovery Rate
fp

False Positives
binary_params

Binary Classification Parameters
maxae

Max Absolute Error
mape

Mean Absolute Percent Error
logloss

Log Loss