Description Usage Arguments Value Note See Also
This function predicts values based upon a model trained
by wsvm
.
1 2 3 4 |
object |
Object of class |
newdata |
An object containing the new input data:
either a matrix or a sparse matrix (object of class
|
decision.values |
Logical controlling whether the decision values of all binary classifiers computed in multiclass classification shall be computed and returned. |
probability |
Logical indicating whether class
probabilities should be computed and returned. Only
possible if the model was fitted with the
|
na.action |
A function to specify the action to be
taken if ‘NA’s are found. The default action is
|
... |
Currently not used. |
A vector of predicted values (for classification: a
vector of labels, for density estimation: a logical
vector). If decision.value
is TRUE
, the
vector gets a "decision.values"
attribute
containing a n * c matrix (n number of
predicted values, c number of classifiers) of all
c binary classifiers' decision values. There are
k * (k - 1) / 2 classifiers (k number of
classes). The colnames of the matrix indicate the labels
of the two classes. If probability
is TRUE
,
the vector gets a "probabilities"
attribute
containing a n * k matrix (n number of
predicted values, k number of classes) of the class
probabilities.
If the training set was scaled by wsvm
(done by
default), the new data is scaled accordingly using scale
and center of the training data.
wsvm
, predict.svm
,
svm
.
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