ppforest2::RegressionForest Class Reference

Random forest of regression trees. More...

#include <RegressionForest.hpp>

Public Types
using	FeatureMatrix = types::FeatureMatrix
using	FeatureVector = types::FeatureVector
using	Outcome = types::Outcome
using	OutcomeVector = types::OutcomeVector
using	Ptr = std::unique_ptr<RegressionForest>
Public Types inherited from ppforest2::Forest
using	FeatureMatrix = types::FeatureMatrix
using	FeatureVector = types::FeatureVector
using	Outcome = types::Outcome
using	OutcomeVector = types::OutcomeVector
using	Ptr = std::unique_ptr<Forest>
using	RNG = stats::RNG
Public Types inherited from ppforest2::Model
using	Ptr = std::shared_ptr<Model>

Public Member Functions
	RegressionForest ()
	RegressionForest (TrainingSpec::Ptr spec)
void	accept (Model::Visitor &visitor) const override
	Accept a model visitor (mode-specific dispatch).
types::Outcome	predict (FeatureVector const &x) const override
	Per-row prediction (mode-specific: majority vote or mean).
virtual types::OutcomeVector	predict (types::FeatureMatrix const &x) const
	Predict a matrix of observations.
Public Member Functions inherited from ppforest2::Forest
void	add_tree (BaggedTree::Ptr tree)
	Add a trained bagged tree to the forest.
bool	operator!= (Forest const &other) const
bool	operator== (Forest const &other) const
Public Member Functions inherited from ppforest2::Model
virtual	~Model ()=default

Static Public Member Functions
static Ptr	train (TrainingSpec const &spec, FeatureMatrix const &x, OutcomeVector const &y)
Static Public Member Functions inherited from ppforest2::Forest
static Ptr	train (TrainingSpec const &spec, FeatureMatrix const &x, OutcomeVector const &y)
	Train a random forest.
Static Public Member Functions inherited from ppforest2::Model
static void	check_train_inputs (types::FeatureMatrix const &x, types::OutcomeVector const &y)
	Validate common training inputs (y non-empty, matching x rows).
static Ptr	train (TrainingSpec const &spec, types::FeatureMatrix &x, types::OutcomeVector &y)
	Train a model from a training specification.

Protected Member Functions
BaggedTree::Ptr	train_tree (FeatureMatrix const &x, OutcomeVector const &y, stats::RNG &rng) const override
	Train one bagged tree on a bootstrap resample of x / y.
Protected Member Functions inherited from ppforest2::Forest
	Forest ()=default
	Forest (TrainingSpec::Ptr spec)
void	build_trees (types::FeatureMatrix const &x, types::OutcomeVector const &y)
	Build training_spec->size bagged trees in parallel and attach them to this forest.

Additional Inherited Members
Public Attributes inherited from ppforest2::Forest
std::vector< BaggedTree::Ptr >	trees
	Bootstrap-aggregated trees. Each BaggedTree pairs the polymorphic inner Tree with its sample indices.
Public Attributes inherited from ppforest2::Model
bool	degenerate = false
	Whether the model contains degenerate nodes/splits.
TrainingSpec::Ptr	training_spec
	Training specification used to build this model.

Detailed Description

Random forest of regression trees.

Aggregates per-tree predictions by arithmetic mean. Use the free functions in Metrics.hpp for OOB diagnostics and variable importance.

Member Typedef Documentation

FeatureMatrix

using ppforest2::RegressionForest::FeatureMatrix = types::FeatureMatrix

FeatureVector

using ppforest2::RegressionForest::FeatureVector = types::FeatureVector

Outcome

using ppforest2::RegressionForest::Outcome = types::Outcome

OutcomeVector

using ppforest2::RegressionForest::OutcomeVector = types::OutcomeVector

Ptr

using ppforest2::RegressionForest::Ptr = std::unique_ptr<RegressionForest>

Constructor & Destructor Documentation

RegressionForest() [1/2]

ppforest2::RegressionForest::RegressionForest

(

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RegressionForest() [2/2]

ppforest2::RegressionForest::RegressionForest ( TrainingSpec::Ptr spec )

explicit

Member Function Documentation

accept()

void ppforest2::RegressionForest::accept ( Model::Visitor & visitor ) const

overridevirtual

Accept a model visitor (mode-specific dispatch).

Implements ppforest2::Forest.

predict() [1/2]

types::Outcome ppforest2::RegressionForest::predict ( FeatureVector const & x ) const

overridevirtual

Per-row prediction (mode-specific: majority vote or mean).

Implements ppforest2::Forest.

predict() [2/2]

virtual types::OutcomeVector ppforest2::Model::predict ( types::FeatureMatrix const & x ) const

inlinevirtual

Predict a matrix of observations.

Default implementation iterates rows and dispatches to the single-row predict. Subclasses may override to vectorize.

Parameters

x	Feature matrix (n × p).

Returns

Predicted group labels (n).

Reimplemented from ppforest2::Forest.

train()

static Ptr ppforest2::RegressionForest::train ( TrainingSpec const & spec, FeatureMatrix const & x, OutcomeVector const & y )

static

train_tree()

BaggedTree::Ptr ppforest2::RegressionForest::train_tree ( FeatureMatrix const & x, OutcomeVector const & y, stats::RNG & rng ) const

overrideprotectedvirtual

Train one bagged tree on a bootstrap resample of x / y.

Mode-specific hook invoked by build_trees once per slot in the forest (with retries on degenerate trees). Subclasses that need per-training shared state (e.g. ClassificationForest caches the parent's label partition for stratified sampling) hold it as a private transient pointer set up before build_trees runs.

Called from inside build_trees's OpenMP parallel for, so implementations must be thread-safe and read-only over *this.

Implements ppforest2::Forest.

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

• core/src/models/RegressionForest.hpp