ml::DecisionTree< Y > Class Template Reference

Decision tree. More...

#include <DecisionTree.hpp>

Public Types
typedef Eigen::Ref< const Eigen::VectorXd >	arg_type
typedef Y	value_type
typedef DecisionTrees::Node< Y >	Node
typedef DecisionTrees::SplitNode< Y >	SplitNode
typedef DecisionTrees::LeafNode< Y >	LeafNode

Public Member Functions
	DecisionTree (std::unique_ptr< Node > &&root)
	Constructs a decision tree by taking ownership of a root node. More...
	DecisionTree (DecisionTree< Y > &&other) noexcept
	Move constructor. More...
	DecisionTree (const DecisionTree< Y > &other)
	Copy constructor. More...
DecisionTree< Y > &	operator= (DecisionTree< Y > &&other) noexcept
	Move assignment operator. More...
DecisionTree< Y > &	operator= (const DecisionTree< Y > &other)
	Copy assignment operator. More...
Y	operator() (arg_type x) const
	Returns a prediction given a feature vector. More...
unsigned int	count_nodes () const
	Counts nodes in the tree.
unsigned int	count_leaf_nodes () const
	Counts leaf nodes in the tree.
double	original_error () const
	Returns the prediction error for training data before any splits are made.
double	total_leaf_error () const
	Returns the total prediction error for training data after all splits.
double	cost_complexity (double alpha) const
	Calculates cost-complexity measure. More...
bool	remove_weakest_link (const double max_allowed_error_increase)
	Finds the weakest link and removes it, if the error does not increase too much. More...
unsigned int	number_lowest_split_nodes () const
	Counts lowest split nodes. More...

Detailed Description

template<class Y>
class ml::DecisionTree< Y >

Decision tree.

Data points are in columns.

Template Parameters

Y	Type of predicted value (integer for classification, real for regression).

Member Typedef Documentation

arg_type

template<class Y >

typedef Eigen::Ref<const Eigen::VectorXd> ml::DecisionTree< Y >::arg_type

< Type for feature vector.

value_type

template<class Y >

typedef Y ml::DecisionTree< Y >::value_type

Type of predicted value (integer for classification, real for regression).

Node

template<class Y >

typedef DecisionTrees::Node<Y> ml::DecisionTree< Y >::Node

Tree node. Nodes are split (non-terminal) or leaf (terminal).

SplitNode

template<class Y >

typedef DecisionTrees::SplitNode<Y> ml::DecisionTree< Y >::SplitNode

Non-terminal node, which splits data depending on a threshold value of some feature.

LeafNode

template<class Y >

typedef DecisionTrees::LeafNode<Y> ml::DecisionTree< Y >::LeafNode

Terminal node, which returns a constant prediction value for features which ended up on it.

Constructor & Destructor Documentation

DecisionTree() [1/3]

template<class Y >

ml::DecisionTree< Y >::DecisionTree ( std::unique_ptr< Node > &&  root )

inline

Constructs a decision tree by taking ownership of a root node.

Parameters

[in,out]

root

Non-null root node pointer.

Exceptions

std::invalid_argument

If root or root->parent is null.

DecisionTree() [2/3]

template<class Y >

ml::DecisionTree< Y >::DecisionTree ( DecisionTree< Y > &&  other )

inlinenoexcept

Move constructor.

Parameters

[in,out]

other

Moved tree.

DecisionTree() [3/3]

template<class Y >

ml::DecisionTree< Y >::DecisionTree ( const DecisionTree< Y > &  other )

inline

Copy constructor.

Parameters

[in]

other

Copied tree.

Member Function Documentation

operator=() [1/2]

template<class Y >

DecisionTree<Y>& ml::DecisionTree< Y >::operator= ( DecisionTree< Y > &&  other )

inlinenoexcept

Move assignment operator.

Parameters

[in,out]

other

Moved tree.

operator=() [2/2]

template<class Y >

DecisionTree<Y>& ml::DecisionTree< Y >::operator= ( const DecisionTree< Y > &  other )

inline

Copy assignment operator.

Parameters

[in]

other

Copied tree.

operator()()

template<class Y >

Y ml::DecisionTree< Y >::operator() ( arg_type  x ) const

inline

Returns a prediction given a feature vector.

Parameters

[in]

x

Feature vector.

Returns

Predicted value.

cost_complexity()

template<class Y >

double ml::DecisionTree< Y >::cost_complexity ( double  alpha ) const

inline

Calculates cost-complexity measure.

Parameters

[in]

alpha

Complexity penalty per each leaf node.

Returns

Total leaf error plus alpha times the number of leaf nodes.

remove_weakest_link()

template<class Y >

bool ml::DecisionTree< Y >::remove_weakest_link ( const double  max_allowed_error_increase )

inline

Finds the weakest link and removes it, if the error does not increase too much.

A "weakest link" is a split node which can be collapsed with the minimum increase of total_leaf_error(). Only the lowest split node can be a weakest link.

Parameters

[in]

max_allowed_error_increase

Maximum allowed increase in total leaf error.

Returns

Whether a node was removed.

Exceptions

std::domain_error

If max_allowed_error_increase < 0.

number_lowest_split_nodes()

template<class Y >

unsigned int ml::DecisionTree< Y >::number_lowest_split_nodes ( ) const

inline

Counts lowest split nodes.

Returns

Number of lowest split nodes.

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

• ML/DecisionTree.hpp