/**
* <summary> Bagging bootstrap ensemble method that creates individuals for its ensemble by training each classifier on a random
* redistribution of the training set.
* This training method is for a bagged decision tree classifier. 20 percent of the instances are left aside for pruning of the trees
* 80 percent of the instances are used for training the trees. The number of trees (forestSize) is a parameter, and basically
* the method will learn an ensemble of trees as a model.</summary>
*
* <param name="trainSet"> Training data given to the algorithm.</param>
* <param name="parameters">Parameters of the bagging trees algorithm. ensembleSize returns the number of trees in the bagged forest.</param>
*/
public override void Train(InstanceList.InstanceList trainSet, Parameter.Parameter parameters)
{
var forestSize = ((BaggingParameter)parameters).GetEnsembleSize();
var forest = new List <DecisionTree>();
for (var i = 0; i < forestSize; i++)
{
var bootstrap = trainSet.Bootstrap(i);
var tree = new DecisionTree(new DecisionNode(new InstanceList.InstanceList(bootstrap.GetSample()), null, null, false));
forest.Add(tree);
}
model = new TreeEnsembleModel(forest);
}
