curfil::RandomTreeTrain< Instance, FeatureEvaluation, FeatureFunction > Class Template Reference

Class that does the actual training of a tree. More...

#include <random_tree.h>

Public Member Functions
	RandomTreeTrain (int id, size_t numClasses, const TrainingConfiguration &configuration)
	featureCount: K >= 1, the number of random splits to sample and evaluate at each interior node.
void	train (FeatureEvaluation &featureEvaluation, RandomSource &randomSource, const std::vector< std::pair< RandomTreePointer, Samples > > &samplesPerNode, int idNode, int currentLevel=1) const
	Train a single random tree breadth-first.

Detailed Description

template<class Instance, class FeatureEvaluation, class FeatureFunction>
class curfil::RandomTreeTrain< Instance, FeatureEvaluation, FeatureFunction >

Class that does the actual training of a tree.

Definition at line 1135 of file random_tree.h.

Constructor & Destructor Documentation

template<class Instance , class FeatureEvaluation , class FeatureFunction >

curfil::RandomTreeTrain< Instance, FeatureEvaluation, FeatureFunction >::RandomTreeTrain ( int  id, size_t  numClasses, const TrainingConfiguration &  configuration  )

inline

featureCount: K >= 1, the number of random splits to sample and evaluate at each interior node.

Note that K=1 yields completely randomized trees, whereas large values of K lead to aggressive optimization of the split. A common choice is round(sqrt(N)), where N is the number of sample instances in the training set passed to the Train method. minSampleCount: n_min, the minimum number of training samples necessary such that training is continued. maxTreeDepth: if < 0, no limit on the tree depth is assumed. If > 0, the tree learning is stopped when the given depth is reached.

Definition at line 1147 of file random_tree.h.

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The documentation for this class was generated from the following file:

• src/curfil/random_tree.h