EDeepLearningDefectDetectionMetrics::GetROCPoint
ROC (Receiver Operating Characteristic) point.
A ROC point is a point from the ROC curve which is the plot of the true positive rate against the false positive rate (see EConfusionMatrixElement) obtained at various classification threshold (see EUnsupervisedSegmenter::ClassificationThreshold).
The ROC points are stricly ordered by decreasing threshold order meaning the true positive rate and false positive rate (see EConfusionMatrixElement) are sorted in increasing order.
Namespace: Euresys::Open_eVision::EasyDeepLearning
[C++]
EROCPoint GetROCPoint(
int index
)
Parameters
index
The index of the classifier to use. If the index is equal to '-1', the index corresponding to EUnsupervisedSegmenter::ClassificationThreshold will be used.
Remarks
Each ROC point corresponds to a different classifier (see EDeepLearningDefectDetectionMetrics::NumberOfClassifiers).
It means that the ROC curve is the perfect tool to choose a threshold depending on the false and true positive rate values that best suit your application.
EDeepLearningDefectDetectionMetrics.GetROCPoint
ROC (Receiver Operating Characteristic) point.
A ROC point is a point from the ROC curve which is the plot of the true positive rate against the false positive rate (see EConfusionMatrixElement) obtained at various classification threshold (see EUnsupervisedSegmenter::ClassificationThreshold).
The ROC points are stricly ordered by decreasing threshold order meaning the true positive rate and false positive rate (see EConfusionMatrixElement) are sorted in increasing order.
Namespace: Euresys.Open_eVision.EasyDeepLearning
[C#]
Euresys.Open_eVision.EasyDeepLearning.EROCPoint GetROCPoint(
int index
)
Parameters
index
The index of the classifier to use. If the index is equal to '-1', the index corresponding to EUnsupervisedSegmenter::ClassificationThreshold will be used.
Remarks
Each ROC point corresponds to a different classifier (see EDeepLearningDefectDetectionMetrics::NumberOfClassifiers).
It means that the ROC curve is the perfect tool to choose a threshold depending on the false and true positive rate values that best suit your application.