Helper functions and classes for decision trees. More...

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

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

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

Functions
std::tuple< RegressionTree, double, double >	regression_tree_auto_prune (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y, unsigned int max_split_levels, unsigned int min_sample_size, const std::vector< double > &alphas, const unsigned int num_folds)
	Grows a regression tree with pruning. More...

std::tuple< ClassificationTree, double, double >	classification_tree_auto_prune (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y, unsigned int max_split_levels, unsigned int min_sample_size, const std::vector< double > &alphas, const unsigned int num_folds)
	Grows a classification tree with pruning. More...

RegressionTree	regression_tree (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y, unsigned int max_split_levels, unsigned int min_sample_size)
	Grows a regression tree without pruning. More...

ClassificationTree	classification_tree (Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y, unsigned int max_split_levels, unsigned int min_sample_size)
	Grows a classification tree without pruning. More...

template<typename Y >
void	cost_complexity_prune (DecisionTree< Y > &tree, const double alpha)
	Performs cost-complexity pruning in-place. More...

double	regression_tree_mean_squared_error (const RegressionTree &tree, Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y)
	Calculates tree mean squared error (MSE) for a sample. More...

double	classification_tree_accuracy (const ClassificationTree &tree, Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y)
	Calculates tree accuracy for a sample. More...

double	classification_tree_misclassification_rate (const ClassificationTree &tree, Eigen::Ref< const Eigen::MatrixXd > X, Eigen::Ref< const Eigen::VectorXd > y)
	Calculates tree misclassification rate for a sample. More...

Detailed Description

Helper functions and classes for decision trees.

Functions for manipulating decision trees.

Function Documentation

◆ regression_tree_auto_prune()

std::tuple<RegressionTree, double, double> ml::DecisionTrees::regression_tree_auto_prune	(	Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y,
		unsigned int	max_split_levels,
		unsigned int	min_sample_size,
		const std::vector< double > &	alphas,
		const unsigned int	num_folds
	)

Grows a regression tree with pruning.

Parameters

[in]	X	Independent variables (column-wise).
[in]	y	Dependent variable.
[in]	max_split_levels	Maximum number of split nodes on the way to any leaf node.
[in]	min_sample_size	Minimum sample size which can be split (at least 2).
[in]	alphas	Candidate alphas for pruning to be selected by cross-validation. If this vector is empty, no pruning is done. If it has just one element, this value is used for pruning. If it has more than one, the one with smallest k-fold cross-validation test error is used.
[in]	num_folds	Number of folds for cross-validation. Ignored if cross-validation is not done.

Returns: Tuple of: trained regression tree, chosen alpha (NaN if no pruning was done) and minimum cross-validation test error (NaN if no cross-validation was done).

Exceptions

std::invalid_argument If min_sample_size < 2, y.size() < 2 or X.cols() != y.size().

◆ classification_tree_auto_prune()

std::tuple<ClassificationTree, double, double> ml::DecisionTrees::classification_tree_auto_prune	(	Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y,
		unsigned int	max_split_levels,
		unsigned int	min_sample_size,
		const std::vector< double > &	alphas,
		const unsigned int	num_folds
	)

Grows a classification tree with pruning.

Parameters

[in]	X	Features (column-wise).
[in]	y	Class indices.
[in]	max_split_levels	Maximum number of split nodes on the way to any leaf node.
[in]	min_sample_size	Minimum sample size which can be split (at least 2).
[in]	alphas	Candidate alphas for pruning to be selected by cross-validation. If this vector is empty, no pruning is done. If it has just one element, this value is used for pruning. If it has more than one, the one with smallest k-fold cross-validation test error is used.
[in]	num_folds	Number of folds for cross-validation. Ignored if cross-validation is not done.

Returns: Tuple of: trained classification tree, chosen alpha (NaN if no pruning was done) and minimum cross-validation test error (NaN if no cross-validation was done).

Exceptions

std::invalid_argument If min_sample_size < 2, y.size() < 2 or X.cols() != y.size().

◆ regression_tree()

RegressionTree ml::DecisionTrees::regression_tree	(	Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y,
		unsigned int	max_split_levels,
		unsigned int	min_sample_size
	)

Grows a regression tree without pruning.

Parameters

[in]	X	Independent variables (column-wise).
[in]	y	Dependent variable.
[in]	max_split_levels	Maximum number of split nodes on the way to any leaf node.
[in]	min_sample_size	Minimum sample size which can be split (at least 2).

Returns: Trained regression tree.

Exceptions

std::invalid_argument If min_sample_size < 2, y.size() < 2 or X.cols() != y.size().

◆ classification_tree()

ClassificationTree ml::DecisionTrees::classification_tree	(	Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y,
		unsigned int	max_split_levels,
		unsigned int	min_sample_size
	)

Grows a classification tree without pruning.

Parameters

[in]	X	Classification features (column-wise).
[in]	y	Class indices.
[in]	max_split_levels	Maximum number of split nodes on the way to any leaf node.
[in]	min_sample_size	Minimum sample size which can be split (at least 2).

Returns: Trained classification tree.

Exceptions

std::invalid_argument If min_sample_size < 2, y.size() < 2 or X.cols() != y.size().

◆ cost_complexity_prune()

template<typename Y >

void ml::DecisionTrees::cost_complexity_prune	(	DecisionTree< Y > &	tree,
		const double	alpha
	)

Performs cost-complexity pruning in-place.

Parameters

[in,out]	tree	Tree to be pruned.
[in]	alpha	Cost of complexity per node.

Template Parameters

Y	Decision tree output value type.

Exceptions

std::domain_error If alpha < 0.

◆ regression_tree_mean_squared_error()

double ml::DecisionTrees::regression_tree_mean_squared_error	(	const RegressionTree &	tree,
		Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y
	)

Calculates tree mean squared error (MSE) for a sample.

MSE for tree \( f \) is defined as \( N^{-1} \sum_{i=1}^N (y_i - f(\vec{X}_i))^2 \).

Parameters

[in]	tree	Regression tree.
[in]	X	Independent variables (column-wise).
[in]	y	Dependent variable.

Returns: MSE or NaN if y.size() == 0.

Exceptions

std::invalid_argument If X.cols() != y.size().

◆ classification_tree_accuracy()

double ml::DecisionTrees::classification_tree_accuracy	(	const ClassificationTree &	tree,
		Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y
	)

Calculates tree accuracy for a sample.

Accuracy for classification tree \( f \) is defined as \( N^{-1} \sum_{i=1}^N \mathbb{1}_{y_i = f(\vec{X}_i)} \).

Parameters

[in]	tree	tree Classification tree.
[in]	X	Classification features (column-wise).
[in]	y	Class indices.

Exceptions

std::invalid_argument If X.cols() != y.size().

◆ classification_tree_misclassification_rate()

double ml::DecisionTrees::classification_tree_misclassification_rate	(	const ClassificationTree &	tree,
		Eigen::Ref< const Eigen::MatrixXd >	X,
		Eigen::Ref< const Eigen::VectorXd >	y
	)

inline

Calculates tree misclassification rate for a sample.

Misclassification rate for classification tree \( f \) is defined as \( N^{-1} \sum_{i=1}^N \mathbb{1}_{y_i \neq f(\vec{X}_i)} \).

Parameters

[in]	tree	Classification tree.
[in]	X	Classification features (column-wise).
[in]	y	Class indices.

Exceptions

std::invalid_argument If X.cols() != y.size().

Classes

Functions

Detailed Description

Function Documentation

◆ regression_tree_auto_prune()

◆ classification_tree_auto_prune()

◆ regression_tree()

◆ classification_tree()

◆ cost_complexity_prune()

◆ regression_tree_mean_squared_error()

◆ classification_tree_accuracy()

◆ classification_tree_misclassification_rate()