[ VIGRA Homepage | Function Index | Class Index | Namespaces | File List | Main Page ]
#include <vigra/random_forest/rf_visitors.hxx>
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) |
VariableImportanceVisitor (int rep_cnt=10) | |
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_ |
calculate variable importance while learning.
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.
© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
html generated using doxygen and Python
|