The RandomForest class provides an implementation of random forests, described in Breiman's seminal paper: More...

Public Types
typedef DecisionTree< FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType >	DecisionTreeType
	Allow access to the underlying decision tree type. More...

Public Member Functions
	RandomForest ()
	Construct the random forest without any training or specifying the number of trees. More...

template < typename MatType >
	RandomForest (const MatType &dataset, const arma::Row< size_t > &labels, const size_t numClasses, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Create a random forest, training on the given labeled training data with the given number of trees. More...

template < typename MatType >
	RandomForest (const MatType &dataset, const data::DatasetInfo &datasetInfo, const arma::Row< size_t > &labels, const size_t numClasses, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Create a random forest, training on the given labeled training data with the given dataset info and the given number of trees. More...

template < typename MatType >
	RandomForest (const MatType &dataset, const arma::Row< size_t > &labels, const size_t numClasses, const arma::rowvec &weights, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Create a random forest, training on the given weighted labeled training data with the given number of trees. More...

template < typename MatType >
	RandomForest (const MatType &dataset, const data::DatasetInfo &datasetInfo, const arma::Row< size_t > &labels, const size_t numClasses, const arma::rowvec &weights, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Create a random forest, training on the given weighted labeled training data with the given dataset info and the given number of trees. More...

template < typename VecType >
size_t	Classify (const VecType &point) const
	Predict the class of the given point. More...

template < typename VecType >
void	Classify (const VecType &point, size_t &prediction, arma::vec &probabilities) const
	Predict the class of the given point and return the predicted class probabilities for each class. More...

template < typename MatType >
void	Classify (const MatType &data, arma::Row< size_t > &predictions) const
	Predict the classes of each point in the given dataset. More...

template < typename MatType >
void	Classify (const MatType &data, arma::Row< size_t > &predictions, arma::mat &probabilities) const
	Predict the classes of each point in the given dataset, also returning the predicted class probabilities for each point. More...

size_t	NumTrees () const
	Get the number of trees in the forest. More...

template < typename Archive >
void	serialize (Archive &ar, const uint32_t)
	Serialize the random forest. More...

template < typename MatType >
double	Train (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, const bool warmStart=false, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Train the random forest on the given labeled training data with the given number of trees. More...

template < typename MatType >
double	Train (const MatType &data, const data::DatasetInfo &datasetInfo, const arma::Row< size_t > &labels, const size_t numClasses, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, const bool warmStart=false, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Train the random forest on the given labeled training data with the given dataset info and the given number of trees. More...

template < typename MatType >
double	Train (const MatType &data, const arma::Row< size_t > &labels, const size_t numClasses, const arma::rowvec &weights, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, const bool warmStart=false, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Train the random forest on the given weighted labeled training data with the given number of trees. More...

template < typename MatType >
double	Train (const MatType &data, const data::DatasetInfo &datasetInfo, const arma::Row< size_t > &labels, const size_t numClasses, const arma::rowvec &weights, const size_t numTrees=20, const size_t minimumLeafSize=1, const double minimumGainSplit=1e-7, const size_t maximumDepth=0, const bool warmStart=false, DimensionSelectionType dimensionSelector=DimensionSelectionType())
	Train the random forest on the given weighted labeled training data with the given dataset info and the given number of trees. More...

const DecisionTreeType &	Tree (const size_t i) const
	Access a tree in the forest. More...

DecisionTreeType &	Tree (const size_t i)
	Modify a tree in the forest (be careful!). More...

Detailed Description

template<typename FitnessFunction = GiniGain, typename DimensionSelectionType = MultipleRandomDimensionSelect, template< typename > class NumericSplitType = BestBinaryNumericSplit, template< typename > class CategoricalSplitType = AllCategoricalSplit, bool UseBootstrap = true>
class mlpack::tree::RandomForest< FitnessFunction, DimensionSelectionType, NumericSplitType, CategoricalSplitType, UseBootstrap >

The RandomForest class provides an implementation of random forests, described in Breiman's seminal paper:

@article{breiman2001random,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine Learning},
  volume={45},
  number={1},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

Definition at line 44 of file random_forest.hpp.

Member Typedef Documentation

◆ DecisionTreeType

typedef DecisionTree<FitnessFunction, NumericSplitType, CategoricalSplitType, DimensionSelectionType> DecisionTreeType

Allow access to the underlying decision tree type.

Definition at line 49 of file random_forest.hpp.

Constructor & Destructor Documentation

◆ RandomForest() [1/5]

RandomForest ( )

Construct the random forest without any training or specifying the number of trees.

Predict() will throw an exception until Train() is called.

◆ RandomForest() [2/5]

RandomForest	(	const MatType &	dataset,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Create a random forest, training on the given labeled training data with the given number of trees.

The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building. Optionally, you may specify a DimensionSelectionType to set parameters for the strategy used to choose dimensions.

Parameters

dataset	Dataset to train on.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ RandomForest() [3/5]

RandomForest	(	const MatType &	dataset,
		const data::DatasetInfo &	datasetInfo,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Create a random forest, training on the given labeled training data with the given dataset info and the given number of trees.

The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building. Optionally, you may specify a DimensionSelectionType to set parameters for the strategy used to choose dimensions. This constructor can be used to train on categorical data.

Parameters

dataset	Dataset to train on.
datasetInfo	Dimension info for the dataset.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ RandomForest() [4/5]

RandomForest	(	const MatType &	dataset,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const arma::rowvec &	weights,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Create a random forest, training on the given weighted labeled training data with the given number of trees.

The minimumLeafSize parameter is given to each individual decision tree during tree building.

Parameters

dataset	Dataset to train on.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
weights	Weights (importances) of each point in the dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

◆ RandomForest() [5/5]

RandomForest	(	const MatType &	dataset,
		const data::DatasetInfo &	datasetInfo,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const arma::rowvec &	weights,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Create a random forest, training on the given weighted labeled training data with the given dataset info and the given number of trees.

The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building. Optionally, you may specify a DimensionSelectionType to set parameters for the strategy used to choose dimensions. This can be used for categorical weighted training.

Parameters

dataset	Dataset to train on.
datasetInfo	Dimension info for the dataset.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
weights	Weights (importances) of each point in the dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
dimensionSelector	Instantiated dimension selection policy.

Member Function Documentation

◆ Classify() [1/4]

size_t Classify ( const VecType & point ) const

Predict the class of the given point.

If the random forest has not been trained, this will throw an exception.

Parameters

point Point to be classified.

◆ Classify() [2/4]

void Classify	(	const VecType &	point,
		size_t &	prediction,
		arma::vec &	probabilities
	)		const

Predict the class of the given point and return the predicted class probabilities for each class.

If the random forest has not been trained, this will throw an exception.

Parameters

point	Point to be classified.
prediction	size_t to store predicted class in.
probabilities	Output vector of class probabilities.

◆ Classify() [3/4]

void Classify	(	const MatType &	data,
		arma::Row< size_t > &	predictions
	)		const

Predict the classes of each point in the given dataset.

If the random forest has not been trained, this will throw an exception.

Parameters

data	Dataset to be classified.
predictions	Output predictions for each point in the dataset.

◆ Classify() [4/4]

void Classify	(	const MatType &	data,
		arma::Row< size_t > &	predictions,
		arma::mat &	probabilities
	)		const

Predict the classes of each point in the given dataset, also returning the predicted class probabilities for each point.

If the random forest has not been trained, this will throw an exception.

Parameters

data	Dataset to be classified.
predictions	Output predictions for each point in the dataset.
probabilities	Output matrix of class probabilities for each point.

◆ NumTrees()

size_t NumTrees ( ) const

inline

Get the number of trees in the forest.

Definition at line 362 of file random_forest.hpp.

References RandomForest< FitnessFunction, DimensionSelectionType, NumericSplitType, CategoricalSplitType, UseBootstrap >::serialize(), and RandomForest< FitnessFunction, DimensionSelectionType, NumericSplitType, CategoricalSplitType, UseBootstrap >::Train().

◆ serialize()

void serialize	(	Archive &	ar,
		const uint32_t
	)

Serialize the random forest.

Referenced by RandomForest< FitnessFunction, DimensionSelectionType, NumericSplitType, CategoricalSplitType, UseBootstrap >::NumTrees().

◆ Train() [1/4]

double Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		const bool	warmStart = `false`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Train the random forest on the given labeled training data with the given number of trees.

The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building. Optionally, you may specify a DimensionSelectionType to set parameters for the strategy used to choose dimensions.

Parameters

data	Dataset to train on.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
warmStart	When set to `true`, it adds `numTrees` new trees to the existing random forest otherwise a new forest is trained from scratch.
dimensionSelector	Instantiated dimension selection policy.

Returns: The average entropy of all the decision trees trained under forest.

Referenced by RandomForest< FitnessFunction, DimensionSelectionType, NumericSplitType, CategoricalSplitType, UseBootstrap >::NumTrees().

◆ Train() [2/4]

double Train	(	const MatType &	data,
		const data::DatasetInfo &	datasetInfo,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		const bool	warmStart = `false`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Train the random forest on the given labeled training data with the given dataset info and the given number of trees.

The minimumLeafSize parameter is given to each individual decision tree during tree building. Optionally, you may specify a DimensionSelectionType to set parameters for the strategy used to choose dimensions. This overload can be used to train on categorical data.

Parameters

data	Dataset to train on.
datasetInfo	Dimension info for the dataset.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
warmStart	When set to `true`, it adds `numTrees` new trees to the existing random forest else a new forest is trained from scratch.
dimensionSelector	Instantiated dimension selection policy.

Returns: The average entropy of all the decision trees trained under forest.

◆ Train() [3/4]

double Train	(	const MatType &	data,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const arma::rowvec &	weights,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		const bool	warmStart = `false`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Train the random forest on the given weighted labeled training data with the given number of trees.

The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building. Optionally, you may specify a DimensionSelectionType to set parameters for the strategy used to choose dimensions.

Parameters

data	Dataset to train on.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
weights	Weights (importances) of each point in the dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
warmStart	When set to `true`, it adds `numTrees` new trees to the existing random forest else a new forest is trained from scratch.
dimensionSelector	Instantiated dimension selection policy.

Returns: The average entropy of all the decision trees trained under forest.

◆ Train() [4/4]

double Train	(	const MatType &	data,
		const data::DatasetInfo &	datasetInfo,
		const arma::Row< size_t > &	labels,
		const size_t	numClasses,
		const arma::rowvec &	weights,
		const size_t	numTrees = `20`,
		const size_t	minimumLeafSize = `1`,
		const double	minimumGainSplit = `1e-7`,
		const size_t	maximumDepth = `0`,
		const bool	warmStart = `false`,
		DimensionSelectionType	dimensionSelector = `DimensionSelectionType()`
	)

Train the random forest on the given weighted labeled training data with the given dataset info and the given number of trees.

The minimumLeafSize and minimumGainSplit parameters are given to each individual decision tree during tree building. Optionally, you may specify a DimensionSelectionType to set parameters for the strategy used to choose dimensions. This overload can be used for categorical weighted training.

Parameters

data	Dataset to train on.
datasetInfo	Dimension info for the dataset.
labels	Labels for dataset.
numClasses	Number of classes in dataset.
weights	Weights (importances) of each point in the dataset.
numTrees	Number of trees in the forest.
minimumLeafSize	Minimum number of points in each tree's leaf nodes.
minimumGainSplit	Minimum gain for splitting a decision tree node.
maximumDepth	Maximum depth for the tree.
warmStart	When set to `true`, it adds `numTrees` new trees to the existing random forest else a new forest is trained from scratch.
dimensionSelector	Instantiated dimension selection policy.

Returns: The average entropy of all the decision trees trained under forest.

◆ Tree() [1/2]

const DecisionTreeType& Tree ( const size_t i ) const

inline

Access a tree in the forest.

Definition at line 357 of file random_forest.hpp.

◆ Tree() [2/2]

DecisionTreeType& Tree ( const size_t i )

inline

Modify a tree in the forest (be careful!).

Definition at line 359 of file random_forest.hpp.

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

/home/ryan/src/mlpack.org/_src/mlpack-git/src/mlpack/methods/random_forest/random_forest.hpp

Public Types

Public Member Functions

Detailed Description

Member Typedef Documentation

◆ DecisionTreeType

Constructor & Destructor Documentation

◆ RandomForest() [1/5]

◆ RandomForest() [2/5]

◆ RandomForest() [3/5]

◆ RandomForest() [4/5]

◆ RandomForest() [5/5]

Member Function Documentation

◆ Classify() [1/4]

◆ Classify() [2/4]

◆ Classify() [3/4]

◆ Classify() [4/4]

◆ NumTrees()

◆ serialize()

◆ Train() [1/4]

◆ Train() [2/4]

◆ Train() [3/4]

◆ Train() [4/4]

◆ Tree() [1/2]

◆ Tree() [2/2]