CGAL::Classification::Evaluation Class Reference

#include <CGAL/Classification/Evaluation.h>

Definition

Class to compute several measurements to evaluate the quality of a classification output.

Constructor
template<typename LabelIndexRange >
	Evaluation (const Label_set &labels, const LabelIndexRange &ground_truth, const LabelIndexRange &result)
	Instantiates an evaluation object and computes all measurements. More...

Label Evaluation
float	precision (Label_handle label) const
	Returns the precision of the training for the given label. More...
float	recall (Label_handle label) const
	Returns the recall of the training for the given label. More...
float	f1_score (Label_handle label) const
	Returns the \(F_1\) score of the training for the given label. More...
float	intersection_over_union (Label_handle label) const
	Returns the intersection over union of the training for the given label. More...

Global Evaluation
float	accuracy () const
	Returns the accuracy of the training. More...
float	mean_f1_score () const
	Returns the mean \(F_1\) score of the training over all labels (see f1_score()).
float	mean_intersection_over_union () const
	Returns the mean intersection over union of the training over all labels (see intersection_over_union()).

Constructor & Destructor Documentation

Evaluation()

template<typename LabelIndexRange >

CGAL::Classification::Evaluation::Evaluation	(	const Label_set &	labels,
		const LabelIndexRange &	ground_truth,
		const LabelIndexRange &	result
	)

Instantiates an evaluation object and computes all measurements.

Parameters

labels	labels used.
ground_truth	vector of label indices: it should contain 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.
result	similar to ground_truth but contained the result of a classification.

Member Function Documentation

accuracy()

float CGAL::Classification::Evaluation::accuracy

(

)

const

Returns the accuracy of the training.

Accuracy is the total number of true positives divided by the total number of provided inliers.

f1_score()

float CGAL::Classification::Evaluation::f1_score

(

Label_handle

label

)

const

Returns the \(F_1\) score of the training for the given label.

\(F_1\) score is the harmonic mean of precision() and recall():

\[ F_1 = 2 \times \frac{precision \times recall}{precision + recall} \]

intersection_over_union()

float CGAL::Classification::Evaluation::intersection_over_union

(

Label_handle

label

)

const

Returns the intersection over union of the training for the given label.

Intersection over union is the number of true positives divided by the sum of the true positives, of the false positives and of the false negatives.

precision()

float CGAL::Classification::Evaluation::precision

(

Label_handle

label

)

const

Returns the precision of the training for the given label.

Precision is the number of true positives divided by the sum of the true positives and the false positives.

recall()

float CGAL::Classification::Evaluation::recall

(

Label_handle

label

)

const

Returns the recall of the training for the given label.

Recall is the number of true positives divided by the sum of the true positives and the false negatives.