Package: mlr3measures 0.6.0

Marc Becker

mlr3measures: Performance Measures for 'mlr3'

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.

Authors:Michel Lang [aut], Martin Binder [ctb], Marc Becker [cre, aut]

mlr3measures_0.6.0.tar.gz
mlr3measures_0.6.0.tar.gz(r-4.5-noble)mlr3measures_0.6.0.tar.gz(r-4.4-noble)
mlr3measures_0.6.0.tgz(r-4.4-emscripten)mlr3measures_0.6.0.tgz(r-4.3-emscripten)
mlr3measures.pdf |mlr3measures.html✨
mlr3measures/json (API)
NEWS

# Install 'mlr3measures' in R:
install.packages('mlr3measures', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mlr-org/mlr3measures/issues

64 exports 4.09 score 3 dependencies 34 dependents 111 scripts 7.4k downloads

Last updated 2 months agofrom:289bc98b9b. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKAug 20 2024
R-4.5-linuxOKAug 20 2024

Exports:accaeapeaucbaccbbrierbiasceconfusion_matrixdorfbetafdrfnfnrfomrfpfprgmeangprjaccardktauloglossmaemapemauc_au1pmauc_au1umauc_aunpmauc_aunumaxaemaxsembriermccmeasuresmedaemedsemsemslenpvone_zeropbiasphippvpraucprecisionraerecallrmsermslerrsersersqsaesesensitivityslesmapespecificitysrhossetntnrtptprzero_one

Dependencies:backportscheckmatePRROC

Readme and manuals

Help Manual

Help pageTopics
mlr3measures: Performance Measures for 'mlr3'mlr3measures-package mlr3measures
Classification Accuracyacc
Absolute Error (per observation)ae
Absolute Percentage Error (per observation)ape
Area Under the ROC Curveauc
Balanced Accuracybacc
Binary Brier Scorebbrier
Biasbias
Classification Errorce
Calculate Binary Confusion Matrixconfusion_matrix
Diagnostic Odds Ratiodor
F-beta Scorefbeta
False Discovery Ratefdr
False Negativesfn
False Negative Ratefnr
False Omission Ratefomr
False Positivesfp
False Positive Ratefpr
Geometric Mean of Recall and Specificitygmean
Geometric Mean of Precision and Recallgpr
Jaccard Similarity Indexjaccard
Kendall's tauktau
Log Losslogloss
Mean Absolute Errormae
Mean Absolute Percent Errormape
Multiclass AUC Scoresmauc_au1p mauc_au1u mauc_aunp mauc_aunu
Max Absolute Errormaxae
Max Squared Errormaxse
Multiclass Brier Scorembrier
Matthews Correlation Coefficientmcc
Measure Registrymeasures
Median Absolute Errormedae
Median Squared Errormedse
Mean Squared Errormse
Mean Squared Log Errormsle
Negative Predictive Valuenpv
Percent Biaspbias
Phi Coefficient Similarityphi
Positive Predictive Valueppv precision
Area Under the Precision-Recall Curveprauc
Relative Absolute Errorrae
Root Mean Squared Errorrmse
Root Mean Squared Log Errorrmsle
Root Relative Squared Errorrrse
Relative Squared Errorrse
R Squaredrsq
Sum of Absolute Errorssae
Squared Error (per observation)se
Squared Log Error (per observation)sle
Symmetric Mean Absolute Percent Errorsmape
Spearman's rhosrho
Sum of Squared Errorssse
True Negativestn
True Negative Ratespecificity tnr
True Positivestp
True Positive Raterecall sensitivity tpr
Zero-One Classification Loss (per observation)one_zero zero_one