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VariableImportance Class Reference | ![]() |
Compute the variable importance. More...
#include <vigra/random_forest_3/random_forest_visitors.hxx>
Public Member Functions | |
template<typename TREE , typename FEATURES , typename LABELS , typename WEIGHTS , typename SCORER , typename ITER > | |
void | visit_after_split (TREE &tree, FEATURES &, LABELS &labels, WEIGHTS &weights, SCORER &scorer, ITER begin, ITER, ITER end) |
template<typename VISITORS , typename RF , typename FEATURES , typename LABELS > | |
void | visit_after_training (VISITORS &visitors, RF &rf, const FEATURES &features, const LABELS &) |
template<typename RF , typename FEATURES , typename LABELS , typename WEIGHTS > | |
void | visit_after_tree (RF &rf, const FEATURES &features, const LABELS &labels, WEIGHTS &) |
template<typename TREE , typename FEATURES , typename LABELS , typename WEIGHTS > | |
void | visit_before_tree (TREE &tree, FEATURES &features, LABELS &, WEIGHTS &weights) |
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void | activate () |
Activate the visitor. | |
void | deactivate () |
Deactivate the visitor. | |
bool | is_active () const |
Return whether the visitor is active or not. | |
template<typename TREE , typename FEATURES , typename LABELS , typename WEIGHTS , typename SCORER , typename ITER > | |
void | visit_after_split (TREE &, FEATURES &, LABELS &, WEIGHTS &, SCORER &, ITER, ITER, ITER) |
Do something after the split was made. | |
template<typename VISITORS , typename RF , typename FEATURES , typename LABELS > | |
void | visit_after_training (VISITORS &, RF &, const FEATURES &, const LABELS &) |
Do something after all trees have been learned. More... | |
template<typename RF , typename FEATURES , typename LABELS , typename WEIGHTS > | |
void | visit_after_tree (RF &, FEATURES &, LABELS &, WEIGHTS &) |
Do something after a tree has been learned. | |
void | visit_before_training () |
Do something before training starts. | |
template<typename TREE , typename FEATURES , typename LABELS , typename WEIGHTS > | |
void | visit_before_tree (TREE &, FEATURES &, LABELS &, WEIGHTS &) |
Do something before a tree has been learned. More... | |
Public Attributes | |
size_t | repetition_count_ |
MultiArray< 2, double > | variable_importance_ |
Compute the variable importance.
void visit_before_tree | ( | TREE & | tree, |
FEATURES & | features, | ||
LABELS & | , | ||
WEIGHTS & | weights | ||
) |
Resize the variable importance array and store in-bag / out-of-bag information.
void visit_after_split | ( | TREE & | tree, |
FEATURES & | , | ||
LABELS & | labels, | ||
WEIGHTS & | weights, | ||
SCORER & | scorer, | ||
ITER | begin, | ||
ITER | , | ||
ITER | end | ||
) |
Calculate the impurity decrease based variable importance after every split.
void visit_after_tree | ( | RF & | rf, |
const FEATURES & | features, | ||
const LABELS & | labels, | ||
WEIGHTS & | |||
) |
Compute the permutation importance.
void visit_after_training | ( | VISITORS & | visitors, |
RF & | rf, | ||
const FEATURES & | features, | ||
const LABELS & | |||
) |
Accumulate the variable importances from the single trees.
MultiArray<2, double> variable_importance_ |
This Array has the same entries as the R - random forest variable importance. Matrix is featureCount by (classCount +2) variable_importance_(ii,jj) is the variable importance measure of the ii-th variable according to: jj = 0 - (classCount-1) classwise permutation importance jj = rowCount(variable_importance_) -2 permutation importance jj = rowCount(variable_importance_) -1 gini decrease importance.
permutation importance: The difference between the fraction of OOB samples classified correctly before and after permuting (randomizing) the ii-th column is calculated. The ii-th column is permuted rep_cnt times.
class wise permutation importance: same as permutation importance. We only look at those OOB samples whose response corresponds to class jj.
gini decrease importance: row ii corresponds to the sum of all gini decreases induced by variable ii in each node of the random forest.
size_t repetition_count_ |
how often the permutation takes place
© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
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