classificationerror
Measures the overall error rate of the classification model in terms of fraction of predictions made by the classifier that are not correct. Interpretation: Classification Error Rate is the fraction of number of in correct predictions out of all examples, where the best value is 1 and the worst is 0.
Attention: Available only with Activate commercial edition.
Syntax
Score = classificationerror(targets,predictions)
Inputs
- targets
- Actual label for each observation.
- predictions
- Predicted value for each observation.
Outputs
- Score
- classificationerror of the classifier.
Example
Usage of classificationerror
targets = [0 1 0 1];
predictions = [1 1 1 1];
score1 = classificationerror(targets, predictions);> score1
score1 = 0.5