CGAL::Classification::OpenCV::Random_forest_classifier Class Reference

#include <CGAL/Classification/OpenCV/Random_forest_classifier.h>

Definition

Classifier based on the OpenCV version of the random forest algorithm.

Note

This class requires the OpenCV library.

Is Model Of:

CGAL::Classification::Classifier

Constructor
	Random_forest_classifier (const Label_set &labels, const Feature_set &features, int max_depth=20, int min_sample_count=5, int max_categories=15, int max_number_of_trees_in_the_forest=100, float forest_accuracy=0.01f)
	instantiates the classifier using the sets of labels and features. More...

Parameters
void	set_max_depth (int max_depth)
void	set_min_sample_count (int min_sample_count)
void	set_max_categories (int max_categories)
void	set_max_number_of_trees_in_the_forest (int max_number_of_trees_in_the_forest)
void	set_forest_accuracy (float forest_accuracy)

Training
template<typename LabelIndexRange >
void	train (const LabelIndexRange &ground_truth)
	runs the training algorithm. More...

Input/Output
void	save_configuration (const char *filename)
	saves the current configuration in the file named filename. More...
void	load_configuration (const char *filename)
	loads a configuration from the file named filename. More...

Constructor & Destructor Documentation

Random_forest_classifier()

CGAL::Classification::OpenCV::Random_forest_classifier::Random_forest_classifier	(	const Label_set &	labels,
		const Feature_set &	features,
		int	max_depth = 20,
		int	min_sample_count = 5,
		int	max_categories = 15,
		int	max_number_of_trees_in_the_forest = 100,
		float	forest_accuracy = 0.01f
	)

instantiates the classifier using the sets of labels and features.

Parameters documentation is copy-pasted from the official documentation of OpenCV. For more details on this method, please refer to it.

Parameters

labels	label set used.
features	feature set used.
max_depth	the depth of the tree. A low value will likely underfit and conversely a high value will likely overfit. The optimal value can be obtained using cross validation or other suitable methods.
min_sample_count	minimum samples required at a leaf node for it to be split. A reasonable value is a small percentage of the total data e.g. 1%.
max_categories	Cluster possible values of a categorical variable into \( K \leq max\_categories \) clusters to find a suboptimal split. If a discrete variable, on which the training procedure tries to make a split, takes more than max_categories values, the precise best subset estimation may take a very long time because the algorithm is exponential. Instead, many decision trees engines (including ML) try to find sub-optimal split in this case by clustering all the samples into max_categories clusters that is some categories are merged together. The clustering is applied only in \( n>2-class \) classification problems for categorical variables with \( N > max\_categories \) possible values. In case of regression and 2-class classification the optimal split can be found efficiently without employing clustering, thus the parameter is not used in these cases.
max_number_of_trees_in_the_forest	The maximum number of trees in the forest (surprise, surprise). Typically the more trees you have the better the accuracy. However, the improvement in accuracy generally diminishes and asymptotes pass a certain number of trees. Also to keep in mind, the number of tree increases the prediction time linearly.
forest_accuracy	Sufficient accuracy (OOB error).

Member Function Documentation

load_configuration()

void CGAL::Classification::OpenCV::Random_forest_classifier::load_configuration

(

const char *

filename

)

loads a configuration from the file named filename.

The input file should be in the XML format written by the save_configuration() method. The feature set of the classifier should contain the exact same features in the exact same order as the ones present when the file was generated using save_configuration().

save_configuration()

void CGAL::Classification::OpenCV::Random_forest_classifier::save_configuration

(

const char *

filename

)

saves the current configuration in the file named filename.

This allows to easily save and recover a specific classification configuration.

The output file is written in an XML format that is readable by the load_configuration() method.

train()

template<typename LabelIndexRange >

void CGAL::Classification::OpenCV::Random_forest_classifier::train

(

const LabelIndexRange &

ground_truth

)

runs the training algorithm.

From the set of provided ground truth, this algorithm estimates sets up the random trees that produce the most accurate result with respect to this ground truth.

Precondition

At least one ground truth item should be assigned to each label.

Parameters

ground_truth

vector of label indices. It should contain for each input item, in the same order as the input set, the index of the corresponding label in the Label_set provided in the constructor. Input items that do not have a ground truth information should be given the value -1.