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CMA
)learnind
:y
:yhat
:prob
:numeric
matrix
whose rows
equals the number of predicted observations (length of y
/yhat
)
and whose columns equal the number of different classes in the learning set.
Rows add up to one.
Entry j,k
of this matrix contains the probability for the j
-th
predicted observation to belong to class k
.
Can be a matrix of NA
s, if the classifier used does not
provide any probabilitiesmethod
:mode
:character
, one of "binary"
(if the number of classes in the learning set is two)
or multiclass
(if it is more than two).model
:show(cloutput-object)
for brief informationftable(cloutput-object)
to obtain a confusion matrix/cross-tabulation
of y
vs. yhat
, s. ftable,cloutput-method
.plot(cloutput-object)
to generate a probability plot of the matrix
prob
described above, s. plot,cloutput-method
roc(cloutput-object)
to compute the empirical ROC curve and the
Area Under the Curve (AUC) based on the predicted probabilities, s.roc,cloutput-method
clvarseloutput
compBoostCMA
, dldaCMA
, ElasticNetCMA
,
fdaCMA
, flexdaCMA
, gbmCMA
,
knnCMA
, ldaCMA
, LassoCMA
,
nnetCMA
, pknnCMA
, plrCMA
,
pls_ldaCMA
, pls_lrCMA
, pls_rfCMA
,
pnnCMA
, qdaCMA
, rfCMA
,
scdaCMA
, shrinkldaCMA
, svmCMA