Contains up to three elements, 'attributesearch' for all params related to the search process, 'attributeeval' for the evaluators, and 'classifier' which has all the arguments for the classifier
Gets a prefix out of the classifier name by stripping all the packages and capital letters - needed to ensure that parameters with the same WEKA name don't collide.
Given a parameter string (generally in the form of a property string), get the testing data; if params is 'default', then this method returns the raw testing data
Given a parameter string (generally in the form of a property string), get the training data; if params is 'default', then this method returns the raw training data
Captures all the data about if this parameter is categorical, numeric (and what the ranges are), and some other helper functions to get some data out of them
Experimental InstanceGenerator that splits the data up into random folds, with a fixed percentage used for training
instanceGeneratorArguments: A property string with the following
startingseed - the initial seed of the splits
numsamples - the number of folds to gererate
percent - the percentage of data to use in the training set
bias - the bias towards a uniform partition
instance string format:
seed - the seed used to split up data
percent - the percentage of data to use in the training set
bias - the bias towards a uniform partition
An implementation of a Wrapper that should ideally just be called from other Java objects - it performes the training/evaluation of a classifier on a sub process to preserve the JVM of the caller