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vigra::rf3 Namespace Reference | ![]() |
Random forest version 3. More...
Classes | |
class | DepthStop |
Random forest 'maximum depth' stop criterion. More... | |
class | EntropyScore |
Functor that computes the entropy score. More... | |
class | GiniScore |
Functor that computes the gini score. More... | |
class | KolmogorovSmirnovScore |
Functor that computes the Kolmogorov-Smirnov score. More... | |
class | NodeComplexityStop |
Random forest 'node complexity' stop criterion. More... | |
class | NumInstancesStop |
Random forest 'number of datapoints' stop criterion. More... | |
class | OOBError |
Compute the out of bag error. More... | |
class | PurityStop |
Random forest 'node purity' stop criterion. More... | |
class | RandomForest |
Random forest version 3. More... | |
class | RandomForestOptions |
Options class for vigra::rf3::RandomForest version 3. More... | |
class | RFStopVisiting |
The default visitor node (= "do nothing"). More... | |
class | RFVisitorBase |
Base class from which all random forest visitors derive. More... | |
class | VariableImportance |
Compute the variable importance. More... | |
struct | VisitorCopy |
Functions | |
template<... > | |
void | random_forest (...) |
Train a vigra::rf3::RandomForest classifier. More... | |
Random forest version 3.
This namespace contains VIGRA's 3rd version of the random forest classification/regression algorithm. This version is much easier to customize than previous versions because it consequently separates algorithms from the forest representation, following the design of the LEMON graph library.
void vigra::rf3::random_forest | ( | ... | ) |
Train a vigra::rf3::RandomForest classifier.
This factory function constructs a vigra::rf3::RandomForest classifier and trains it for the given features and labels. They must be given as a matrix with shape num_instances x num_features
and an array with length num_instances
respectively. Most training options (such as number of trees in the forest, termination and split criteria, and number of threads for parallel training) are specified via an option object of type vigra::rf3::RandomForestOptions. Optional visitors are typically used to compute the out-of-bag error of the classifier (use vigra::rf3::OOBError) and estimate variable importance on the basis of the Gini gain (use vigra::rf3::VariableImportance). You can also provide a specific random number generator instance, which is especially useful when you want to enforce deterministic algorithm behavior during debugging.
Declaration:
Usage:
#include <vigra/random_forest_3.hxx>
Namespace: vigra::rf3
© Ullrich Köthe (ullrich.koethe@iwr.uni-heidelberg.de) |
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