precision
It measures the performance of a classification model in terms of the classifier’s ability to not label a negative example as positive. Precision score can be interpreted as the probability that a positive prediction made by the classifier is positive, where the best value is 1 and the worst is 0.
Syntax
Score = precision(targets,predictions,average)
Inputs
- targets
- Actual label for each observation.
- predictions
- Predicted value for each observation.
- average
- Averaging strategy in case of multiclass classification. 'micro' (default), 'macro', 'none' are the possible values for average. If 'none' is chosen, per class metric is given as output.
Outputs
- Score
- Precision score of the classifier.
Example
Usage of precision
targets = [0, 1, 2, 3, 0, 1, 2, 3];
predictions = [1, 0, 2, 1, 3, 1, 0, 1];
score1 = precision(targets, predictions);
score2 = precision(targets, predictions, 'micro');
score3 = precision(targets, predictions, 'macro');
score4 = precision(targets, predictions, 'none');
printf('Micro: %f \n', score1);
printf('Micro: %f \n', score2);
printf('Macro: %f \n', score3);
printf('None : ');
disp(score4);Micro: 0.250000
Micro: 0.250000
Macro: 0.312500
None :
struct [
0: 0
1: 0.25
2: 1
3: 0
]