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| VariableImportanceVisitor |

`#include <vigra/random_forest/rf_visitors.hxx>`

Inheritance diagram for VariableImportanceVisitor:

List of all members.

## Public Member Functions
template<class RF, class PR, class SM, class ST>
void | after_tree_ip_impl (RF &rf, PR &pr, SM &sm, ST &st, int index)
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_end (RF &rf, PR &pr)
## Public Attributes
MultiArray< 2, double > | variable_importance_

## Detailed Description

calculate variable importance while learning.

## Member Function Documentation

template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
void after_tree_ip_impl | ( | RF & | rf,
|  | PR & | pr,
|  | SM & | sm,
|  | ST & | st,
|  | int | index |
| ) |  |  |

compute permutation based var imp. (Only an Array of size oob_sample_count x 1 is created.

- apposed to oob_sample_count x feature_count in the other method.

See also:
  FieldProxy

template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
template<class Tree, class Split, class Region, class Feature_t, class Label_t>
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 |
| ) |  |  |

calculates impurity decrease based variable importance after every split.

Reimplemented from VisitorBase.

template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
template<class RF, class PR, class SM, class ST>
void visit_after_tree | ( | RF & | rf,
|  | PR & | pr,
|  | SM & | sm,
|  | ST & | st,
|  | int | index |
| ) |  |  |

calculate permutation based impurity after every tree has been learned default behaviour is that this happens out of place. If you have very big data sets and want to avoid copying of data set the in_place_ flag to true.

Reimplemented from VisitorBase.

template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
template<class RF, class PR>
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

MultiArray<2, double> variable_importance_

This Array has the same entries as the R - random forest variable importance

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

- vigra/random_forest/rf_visitors.hxx

© 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 (28 Sep 2010)