A projection pursuit decision tree for regression. More...

#include <RegressionTree.hpp>

Public Types
using	FeatureMatrix = types::FeatureMatrix

using	FeatureVector = types::FeatureVector

using	GroupPartition = stats::GroupPartition

using	Outcome = types::Outcome

using	OutcomeVector = types::OutcomeVector

using	Ptr = std::unique_ptr<RegressionTree>

using	RNG = stats::RNG

Public Types inherited from ppforest2::Tree
using	FeatureMatrix = types::FeatureMatrix

using	FeatureVector = types::FeatureVector

using	GroupPartition = stats::GroupPartition

using	Outcome = types::Outcome

using	OutcomeVector = types::OutcomeVector

using	Ptr = std::unique_ptr<Tree>

using	RNG = stats::RNG

using	Root = TreeNode::Ptr

Public Types inherited from ppforest2::Model
using	Ptr = std::shared_ptr<Model>

Public Member Functions
	RegressionTree (TreeNode::Ptr root, TrainingSpec::Ptr spec)

void	accept (Model::Visitor &visitor) const override
	Accept a model visitor (mode-specific dispatch).

types::Outcome	predict (types::FeatureVector const &x) const override
	Predict a single observation.

Public Member Functions inherited from ppforest2::Tree
bool	operator!= (Tree const &other) const

bool	operator== (Tree const &other) const

virtual types::OutcomeVector	predict (types::FeatureMatrix const &x) const
	Predict a matrix of observations.

types::Outcome	predict (types::FeatureVector const &x) const override
	Predict a single observation.

Public Member Functions inherited from ppforest2::Model
virtual	~Model ()=default

Static Public Member Functions
static Ptr	train (TrainingSpec const &s, FeatureMatrix &x, OutcomeVector &y, GroupPartition const &y_part, RNG &rng)
	Train a regression tree with an external RNG.

Static Public Member Functions inherited from ppforest2::Tree
static Ptr	train (TrainingSpec const &spec, types::FeatureMatrix &x, types::OutcomeVector &y)
	Train a tree from a response vector.

static Ptr	train (TrainingSpec const &spec, types::FeatureMatrix &x, types::OutcomeVector &y, stats::RNG &rng)
	Train a tree from a response vector.

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.

Additional Inherited Members
Public Attributes inherited from ppforest2::Tree
Root	root
	Root node of the tree.

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.

Protected Member Functions inherited from ppforest2::Tree
	Tree (TreeNode::Ptr root, TrainingSpec::Ptr spec)

Static Protected Member Functions inherited from ppforest2::Tree
static Root	build_root (TrainingSpec const &spec, FeatureMatrix &x, OutcomeVector &y, GroupPartition const &y_part, RNG &rng)
	Build the root node of a tree.

Detailed Description

A projection pursuit decision tree for regression.

Leaves hold continuous mean response values produced by the MeanResponse leaf strategy. Training requires a y vector; in-place reordering of feature rows happens inside the build loop via the ByCutpoint grouping strategy.

Member Typedef Documentation

◆ FeatureMatrix

using ppforest2::RegressionTree::FeatureMatrix = types::FeatureMatrix

◆ FeatureVector

using ppforest2::RegressionTree::FeatureVector = types::FeatureVector

◆ GroupPartition

using ppforest2::RegressionTree::GroupPartition = stats::GroupPartition

◆ Outcome

using ppforest2::RegressionTree::Outcome = types::Outcome

◆ OutcomeVector

using ppforest2::RegressionTree::OutcomeVector = types::OutcomeVector

◆ Ptr

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

◆ RNG

using ppforest2::RegressionTree::RNG = stats::RNG

Constructor & Destructor Documentation

◆ RegressionTree()

ppforest2::RegressionTree::RegressionTree	(	TreeNode::Ptr	root,
		TrainingSpec::Ptr	spec )

inline

Member Function Documentation

◆ accept()

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

overridevirtual

Accept a model visitor (mode-specific dispatch).

Implements ppforest2::Tree.

◆ predict()

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

overridevirtual

Predict a single observation.

Walks the tree and returns the leaf value. Same implementation for both modes — the leaf value is produced by the mode-specific leaf strategy during training.

Implements ppforest2::Model.

◆ train()

static Ptr ppforest2::RegressionTree::train	(	TrainingSpec const &	s,
		FeatureMatrix &	x,
		OutcomeVector &	y,
		GroupPartition const &	y_part,
		RNG &	rng )

static

Train a regression tree with an external RNG.

Takes x and y by non-const reference. The ByCutpoint grouping strategy reorders rows in place on the caller's storage — there is no internal copy. Callers must pass buffers they own and are willing to see mutated. Typical callers:

Bootstrap trees: pass freshly-built local subsamples. Zero additional copies beyond the subsample itself.
Single-tree Tree::train path: the top-level dispatcher holds the caller's data as const&, so it makes a single copy of x and y at the call site before invoking this function. The copy is visible at the caller, not hidden inside.

Parameters

s	Training specification (must have `mode = Regression`).
x	Feature matrix (n × p), sorted by continuous response. Will be permuted in place during training.
y	Continuous response vector (n), same order as `x`. Will be permuted in place during training.
y_part	Initial root group partition (typically a median split).
rng	Random number generator.

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

core/src/models/RegressionTree.hpp

Public Types

Public Member Functions

Static Public Member Functions

Additional Inherited Members