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OOB_Error Class Reference

#include <vigra/random_forest/rf_visitors.hxx>

Inheritance diagram for OOB_Error:
VisitorBase

Public Member Functions

template<class RF , class PR , class SM , class ST >
void visit_after_tree (RF &rf, PR &pr, SM &sm, ST &st, int index)
template<class RF , class PR >
void visit_at_end (RF &rf, PR &pr)
- Public Member Functions inherited from VisitorBase
double return_val ()
template<class Tree , class Split , class Region , class Feature_t , class Label_t >
void visit_after_split (Tree &tree, Split &split, Region &parent, Region &leftChild, Region &rightChild, Feature_t &features, Label_t &labels)
template<class RF , class PR >
void visit_at_beginning (RF const &rf, PR const &pr)
template<class RF , class PR >
void visit_at_end (RF const &rf, PR const &pr)
template<class TR , class IntT , class TopT , class Feat >
void visit_external_node (TR &tr, IntT index, TopT node_t, Feat &features)
template<class TR , class IntT , class TopT , class Feat >
void visit_internal_node (TR &tr, IntT index, TopT node_t, Feat &features)

Public Attributes

double oob_breiman

Detailed Description

Visitor that calculates the oob error of the ensemble This rate should be used to estimate the crossvalidation error rate. Here each sample is put down those trees, for which this sample is OOB i.e. if sample #1 is OOB for trees 1, 3 and 5 we calculate the output using the ensemble consisting only of trees 1 3 and 5.

Using normal bagged sampling each sample is OOB for approx. 33% of trees The error rate obtained as such therefore corresponds to crossvalidation rate obtained using a ensemble containing 33% of the trees.


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

© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de)
Heidelberg Collaboratory for Image Processing, University of Heidelberg, Germany

html generated using doxygen and Python
vigra 1.7.1 (Wed Mar 12 2014)