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CompleteOOBInfo Class Reference |  |
#include <vigra/random_forest/rf_visitors.hxx>
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
| void visit_at_end | ( | RF & | rf, |
| PR & | pr | ||
| ) |
Normalise variable importance after the number of trees is known.
Member Data Documentation
| MultiArray<2, double> oob_per_tree |
OOB Error rate of each individual tree
| double oob_mean |
Mean of oob_per_tree
| double oob_std |
Standard deviation of oob_per_tree
| double oob_breiman |
Ensemble OOB error
- See Also
- OOB_Error
| double oob_per_tree2 |
Per Tree OOB error calculated as in OOB_PerTreeError (Ulli's version)
| MultiArray<2, double> breiman_per_tree |
Column containing the development of the Ensemble error rate with increasing number of trees
| 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:
- vigra/random_forest/rf_visitors.hxx