private static RandomForestClassificationSolution GridSearchWithCrossvalidation(IClassificationProblemData problemData, int numberOfCrossvalidationFolds, out RFParameter bestParameters,
int seed = 3141519)
{
double rmsError, outOfBagRmsError, relClassificationError, outOfBagRelClassificationError;
bestParameters = RandomForestUtil.GridSearch(problemData, numberOfFolds, shuffleFolds, randomForestParameterRanges, seed, maximumDegreeOfParallelism);
var model = RandomForestModel.CreateClassificationModel(problemData, problemData.TrainingIndices, bestParameters.N, bestParameters.R, bestParameters.M, seed,
out rmsError, out outOfBagRmsError, out relClassificationError, out outOfBagRelClassificationError);
return((RandomForestClassificationSolution)model.CreateClassificationSolution(problemData));
}
public void RecreateTree()
{
tree = RandomForestModel.trainTree();
}
public string ApplyTree(Tuple <int, string, string, DateTime, double, double, double, Tuple <double, int, string> > data)
{
return(RandomForestModel.applyTree(tree, data));
}
public PredictionLogic()
{
tree = RandomForestModel.trainTree();
}