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Should only be used by experts!
This initializes a svm object and allocates in C++ an SVM model to which it keeps a reference.
init.liquidSVM(x, y, ...)# S3 method for formula
init.liquidSVM(x, y, ..., d = NULL)
# S3 method for default
init.liquidSVM(x, y, scenario = NULL,
useCells = NULL, ..., sampleWeights = NULL, groupIds = NULL,
ids = NULL, d = NULL)
either a formula or the features
either the data or the labels corresponding to the features x.
It can be a character in which case the data is loaded using liquidData.
If it is of type liquidData then after training and selection
the model is tested using the testing data (y$test).
configuration parameters, see Configuration. Can be threads=2, display=1, gpus=1, etc.
level of display information
configures the model for a learning scenario:
E.g. scenario='ls', scenario='mc', scenario='npl', scenario='qt' etc.
Unlike the specialized functions qtSVM, exSVM, nplSVM etc.
this does not trigger the correct select
if TRUE partitions the problem (equivalent to partition_choice=6)
vector of weights for every sample or NULL (default) [currently has no effect]
vector of integer group ids for every sample or NULL (default).
If not NULL this will do group-wise folds, see folds_kind='GROUPED'.
vector of integer ids for every sample or NULL (default) [currently has no effect]
an object of type svm
formula: Initialize SVM model using a a formula and data
default: Initialize SVM model using a data frame and a label vector
Since it binds heap memory it has to be released using clean.liquidSVM which
is also performed at garbage collection.
The training data can either be provided using a formula and a corresponding data.frame
or the features and the labels are given directly.
# NOT RUN {
modelTrees <- init.liquidSVM(Height ~ Girth + Volume, trees[1:20, ]) # least squares
modelIris <- init.liquidSVM(Species ~ ., iris) # multiclass classification
# }
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