All supported measures can be found by listMeasures
or as a table
in the tutorial appendix:
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.
featperctimetrain
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)
brier
measureBrier(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
Measure
,
makeMeasure
; makeCostMeasure
;
makeCustomResampledMeasure
;
performance