#include <shark/Algorithms/Trainers/RFTrainer.h>
Inheritance diagram for shark::RFTrainer< RealVector >:
Public Member Functions | |
| RFTrainer (bool computeFeatureImportances=false, bool computeOOBerror=false) | |
| Construct and compute feature importances when training or not. More... | |
| std::string | name () const |
| From INameable: return the class name. More... | |
| void | setMTry (std::size_t mtry) |
| void | setNTrees (std::size_t numTrees) |
| Set the number of trees to grow. (default 100) More... | |
| void | setMinSplit (std::size_t numSamples) |
| Set Minimum number of samples that is split (default 10) More... | |
| void | setMaxDepth (std::size_t maxDepth) |
| Set Maximum depth of the tree (default 10000) More... | |
| void | setNodeSize (std::size_t nodeSize) |
| void | minImpurity (double impurity) |
| The minimum impurity below which a a node is considere pure (default 1.e-10) More... | |
| void | epsilon (double distance) |
| The minimum dtsnace of features to be considered different (detault 1.e-10) More... | |
| RealVector | parameterVector () const |
| Return the parameter vector. More... | |
| void | setParameterVector (RealVector const &newParameters) |
| Set the parameter vector. More... | |
| void | train (RFClassifier< LabelType > &model, WeightedLabeledData< RealVector, LabelType > const &dataset) |
| Train a random forest for classification. More... | |
| Public Member Functions inherited from shark::AbstractWeightedTrainer< RFClassifier< RealVector > > | |
| virtual void | train (ModelType &model, WeightedDatasetType const &dataset)=0 |
| Executes the algorithm and trains a model on the given weighted data. More... | |
| virtual void | train (ModelType &model, DatasetType const &dataset) |
| Executes the algorithm and trains a model on the given unweighted data. More... | |
| Public Member Functions inherited from shark::INameable | |
| virtual | ~INameable () |
| Public Member Functions inherited from shark::ISerializable | |
| virtual | ~ISerializable () |
| Virtual d'tor. More... | |
| virtual void | read (InArchive &archive) |
| Read the component from the supplied archive. More... | |
| virtual void | write (OutArchive &archive) const |
| Write the component to the supplied archive. More... | |
| void | load (InArchive &archive, unsigned int version) |
| Versioned loading of components, calls read(...). More... | |
| void | save (OutArchive &archive, unsigned int version) const |
| Versioned storing of components, calls write(...). More... | |
| BOOST_SERIALIZATION_SPLIT_MEMBER () | |
| Public Member Functions inherited from shark::IParameterizable< RealVector > | |
| virtual | ~IParameterizable () |
| virtual std::size_t | numberOfParameters () const |
| Return the number of parameters. More... | |
Additional Inherited Members | |
| Public Types inherited from shark::AbstractWeightedTrainer< RFClassifier< RealVector > > | |
| typedef base_type::ModelType | ModelType |
| typedef base_type::InputType | InputType |
| typedef base_type::LabelType | LabelType |
| typedef base_type::DatasetType | DatasetType |
| typedef WeightedLabeledData< InputType, LabelType > | WeightedDatasetType |
| Public Types inherited from shark::IParameterizable< RealVector > | |
| typedef RealVector | ParameterVectorType |
Detailed Description
template<>
class shark::RFTrainer< RealVector >
Definition at line 194 of file RFTrainer.h.
Constructor & Destructor Documentation
◆ RFTrainer()
|
inline |
Construct and compute feature importances when training or not.
Definition at line 199 of file RFTrainer.h.
Member Function Documentation
◆ epsilon()
|
inline |
The minimum dtsnace of features to be considered different (detault 1.e-10)
Definition at line 237 of file RFTrainer.h.
◆ minImpurity()
|
inline |
The minimum impurity below which a a node is considere pure (default 1.e-10)
Definition at line 234 of file RFTrainer.h.
◆ name()
|
inlinevirtual |
From INameable: return the class name.
Reimplemented from shark::INameable.
Definition at line 212 of file RFTrainer.h.
◆ parameterVector()
|
inlinevirtual |
Return the parameter vector.
Reimplemented from shark::IParameterizable< RealVector >.
Definition at line 240 of file RFTrainer.h.
◆ setMaxDepth()
|
inline |
Set Maximum depth of the tree (default 10000)
Definition at line 227 of file RFTrainer.h.
◆ setMinSplit()
|
inline |
Set Minimum number of samples that is split (default 10)
Definition at line 224 of file RFTrainer.h.
◆ setMTry()
|
inline |
Set the number of random attributes to investigate at each node.
Defualt is 0 which is translated to inputDim(data)/3 during training
Definition at line 218 of file RFTrainer.h.
◆ setNodeSize()
|
inline |
Controls when a node is considered pure. If set to 1, a node is pure when it only consists of a single node.(default 5)
Definition at line 231 of file RFTrainer.h.
◆ setNTrees()
|
inline |
Set the number of trees to grow. (default 100)
Definition at line 221 of file RFTrainer.h.
◆ setParameterVector()
|
inlinevirtual |
Set the parameter vector.
Reimplemented from shark::IParameterizable< RealVector >.
Definition at line 243 of file RFTrainer.h.
References SHARK_ASSERT.
◆ train()
|
inline |
Train a random forest for classification.
Definition at line 249 of file RFTrainer.h.
References shark::MeanModel< CARTree< LabelType > >::clearModels(), shark::random::discrete(), shark::Data< Type >::elements(), shark::random::globalRng, shark::inputDimension(), shark::WeightedLabeledData< InputT, LabelT >::inputs(), shark::labelDimension(), shark::WeightedLabeledData< InputT, LabelT >::labels(), and shark::MeanModel< CARTree< LabelType > >::setOutputSize().
The documentation for this class was generated from the following file:
- include/shark/Algorithms/Trainers/RFTrainer.h