mlr (version 2.3)

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: http://berndbischl.github.io/mlr/tutorial/html/measures/.

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).

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)
mmce
measureMMCE(truth, response)
acc
measureACC(truth, response)
ber
multiclass.auc
auc
measureAUC(probabilites, truth, negative, positive)
bac
measureBAC(truth, response, negative, positive)
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)
npv
measureNPV(truth, response, negative)
fdr
measureFDR(truth, response, positive)
mcc
measureMCC(truth, response, negative, positive)
f1
gmean
measureGMEAN(truth, response, negative, positive)
gpr
measureGPR(truth, response, positive)
cindex
meancosts
mcp
db
dunn
G1
G2
silhouette

docType

data

format

none

See Also

Other performance: Measure, makeMeasure; makeCostMeasure; makeCustomResampledMeasure; performance