// Contact <francois.fleuret@idiap.ch> for comments & bug reports //
///////////////////////////////////////////////////////////////////////////
+/*
+
+ An implementation of the classifier with a decision tree. Each node
+ simply thresholds one of the component, and is chosen for maximum
+ loss reduction locally during training. The leaves are labelled with
+ the classifier response, which is chosen again for maximum loss
+ reduction.
+
+ */
+
#ifndef DECISION_TREE_H
#define DECISION_TREE_H
class DecisionTree : public Classifier {
+ static const int min_nb_samples_for_split = 5;
+
int _feature_index;
scalar_t _threshold;
scalar_t _weight;
DecisionTree *_subtree_lesser, *_subtree_greater;
- static const int min_nb_samples_for_split = 5;
-
void pick_best_split(SampleSet *sample_set,
scalar_t *loss_derivatives);