SVM::train
Create a SVMModel based on training data
Description
public SVMModel svm::train(array $problem, array $weights = ?)
Parameters
-
problem
-
The problem can be provided in three different ways.
An array, where the data should start with the class label
(usually 1 or -1) then followed by a sparse data set of
dimension => data pairs.
A URL to a file containing a SVM Light formatted problem, with the
each line being a new training example, the start of each line
containing the class (1, -1) then a series of tab separated data
values shows as key:value.
A opened stream pointing to a data source formatted as in the file above.
-
weights
-
Weights are an optional set of weighting parameters for the different
classes, to help account for unbalanced training sets. For example,
if the classes were 1 and -1, and -1 had significantly more example
than one, the weight for -1 could be 0.5. Weights should be in the range 0-1.
Return Values
Returns an SVMModel that can be used to classify previously unseen data.
Throws SVMException on error