measures: Performance measures.

Description

A performance measure is evaluated after a single train/predict step and returns a single number to assess the quality of the prediction (or maybe only the model, think AIC). The measure itself knows whether it wants to be minimized or maximized and for what tasks it is applicable.

All supported measures can be found by listMeasures or as a table in the tutorial appendix: https://mlr-org.github.io/mlr/articles/measures.html.

If you want a measure for a misclassification cost matrix, look at makeCostMeasure. If you want to implement your own measure, look at makeMeasure.

Most measures can directly be accessed via the function named after the scheme measureX (e.g. measureSSE).

For clustering measures, we compact the predicted cluster IDs such that they form a continuous series starting with 1. If this is not the case, some of the measures will generate warnings.

Some measure have parameters. Their defaults are set in the constructor makeMeasure and can be overwritten using setMeasurePars.

Usage

featperc
timetrain
timepredict
timeboth
sse
measureSSE(truth, response)
mse
measureMSE(truth, response)
rmse
measureRMSE(truth, response)
medse
measureMEDSE(truth, response)
sae
measureSAE(truth, response)
mae
measureMAE(truth, response)
medae
measureMEDAE(truth, response)
rsq
measureRSQ(truth, response)
expvar
measureEXPVAR(truth, response)
arsq
rrse
measureRRSE(truth, response)
rae
measureRAE(truth, response)
mape
measureMAPE(truth, response)
msle
measureMSLE(truth, response)
rmsle
measureRMSLE(truth, response)
kendalltau
measureKendallTau(truth, response)
spearmanrho
measureSpearmanRho(truth, response)
mmce
measureMMCE(truth, response)
acc
measureACC(truth, response)
ber
measureBER(truth, response)
multiclass.aunu
measureAUNU(probabilities, truth)
multiclass.aunp
measureAUNP(probabilities, truth)
multiclass.au1u
measureAU1U(probabilities, truth)
multiclass.au1p
measureAU1P(probabilities, truth)
multiclass.brier
measureMulticlassBrier(probabilities, truth)
logloss
measureLogloss(probabilities, truth)
ssr
measureSSR(probabilities, truth)
qsr
measureQSR(probabilities, truth)
lsr
measureLSR(probabilities, truth)
kappa
measureKAPPA(truth, response)
wkappa
measureWKAPPA(truth, response)
auc
measureAUC(probabilities, truth, negative, positive)
brier
measureBrier(probabilities, truth, negative, positive)
brier.scaled
measureBrierScaled(probabilities, truth, negative, positive)
bac
measureBAC(truth, response)
tp
measureTP(truth, response, positive)
tn
measureTN(truth, response, negative)
fp
measureFP(truth, response, positive)
fn
measureFN(truth, response, negative)
tpr
measureTPR(truth, response, positive)
tnr
measureTNR(truth, response, negative)
fpr
measureFPR(truth, response, negative, positive)
fnr
measureFNR(truth, response, negative, positive)
ppv
measurePPV(truth, response, positive, probabilities = NULL)
npv
measureNPV(truth, response, negative)
fdr
measureFDR(truth, response, positive)
mcc
measureMCC(truth, response, negative, positive)
f1
measureF1(truth, response, positive)
gmean
measureGMEAN(truth, response, negative, positive)
gpr
measureGPR(truth, response, positive)
multilabel.hamloss
measureMultilabelHamloss(truth, response)
multilabel.subset01
measureMultilabelSubset01(truth, response)
multilabel.f1
measureMultilabelF1(truth, response)
multilabel.acc
measureMultilabelACC(truth, response)
multilabel.ppv
measureMultilabelPPV(truth, response)
multilabel.tpr
measureMultilabelTPR(truth, response)
cindex
cindex.uno
iauc.uno
ibrier
meancosts
mcp
db
dunn
G1
G2
silhouette

Arguments

truth

(factor) Vector of the true class.

response

(factor) Vector of the predicted class.

probabilities

(numeric | matrix) a) For purely binary classification measures: The predicted probabilities for the positive class as a numeric vector. b) For multiclass classification measures: The predicted probabilities for all classes, always as a numeric matrix, where columns are named with class labels.

negative

(character(1)) The name of the negative class.

positive

(character(1)) The name of the positive class.

Format

none

References

He, H. & Garcia, E. A. (2009) Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9. pp. 1263-1284.

H. Uno et al. On the C-statistics for Evaluating Overall Adequacy of Risk Prediction Procedures with Censored Survival Data Statistics in medicine. 2011;30(10):1105-1117. https://doi.org/10.1002/sim.4154.

H. Uno et al. Evaluating Prediction Rules for T-Year Survivors with Censored Regression Models Journal of the American Statistical Association 102, no. 478 (2007): 527-37. http://www.jstor.org/stable/27639883.

Description

Usage

Arguments

Format

References

See Also