CGAL 5.1  Classification

#include <CGAL/Classification/Evaluation.h>
Class to compute several measurements to evaluate the quality of a classification output.
Constructor  
template<typename GroundTruthIndexRange , typename ResultIndexRange >  
Evaluation (const Label_set &labels, const GroundTruthIndexRange &ground_truth, const ResultIndexRange &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() ).  
CGAL::Classification::Evaluation::Evaluation  (  const Label_set &  labels, 
const GroundTruthIndexRange &  ground_truth,  
const ResultIndexRange &  result  
) 
Instantiates an evaluation object and computes all measurements.
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. 
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.
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()
:
\[ F_1 = 2 \times \frac{precision \times recall}{precision + recall} \]
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.
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.
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.