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CompleteOOBInfo Class Reference

#include <vigra/random_forest/rf_visitors.hxx>

Inheritance diagram for CompleteOOBInfo:
VisitorBase

Public Member Functions

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, 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_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 &, IntT, TopT, Feat &)
 

Public Attributes

MultiArray< 2, double > breiman_per_tree
 
double oob_breiman
 
double oob_mean
 
MultiArray< 2, double > oob_per_tree
 
double oob_per_tree2
 
double oob_std
 
MultiArray< 4, double > oobroc_per_tree
 

Detailed Description

Visitor that calculates different OOB error statistics

Member Function Documentation

◆ visit_at_end()

template<class RF, class PR>
void visit_at_end ( RF & rf,
PR & pr )

Normalise variable importance after the number of trees is known.

Member Data Documentation

◆ oob_per_tree

MultiArray<2, double> oob_per_tree

OOB Error rate of each individual tree

◆ oob_mean

double oob_mean

Mean of oob_per_tree

◆ oob_std

double oob_std

Standard deviation of oob_per_tree

◆ oob_breiman

double oob_breiman

Ensemble OOB error

See also
OOB_Error

◆ oob_per_tree2

double oob_per_tree2

Per Tree OOB error calculated as in OOB_PerTreeError (Ulli's version)

◆ breiman_per_tree

MultiArray<2, double> breiman_per_tree

Column containing the development of the Ensemble error rate with increasing number of trees

◆ oobroc_per_tree

MultiArray<4, double> oobroc_per_tree

4 dimensional array containing the development of confusion matrices with number of trees - can be used to estimate ROC curves etc.

oobroc_per_tree(ii,jj,kk,ll) corresponds true label = ii predicted label = jj confusion matrix after ll trees

explanation of third index:

Two class case: kk = 0 - (treeCount-1) Threshold is on Probability for class 0 is kk/(treeCount-1); More classes: kk = 0. Threshold on probability set by argMax of the probability array.


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

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vigra 1.12.2 (Mon Apr 14 2025)