EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracy
Best achievable weighted accuracy.
The weighted accuracy is the weighted average of the true positive rate and the true negative rate (which is equal to 1 minus the false positive rate). See EROCPoint.
The classification threshold corresponding to this accuracy is given by EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold.
Namespace: Euresys::Open_eVision::EasyDeepLearning
[C++]
float GetBestWeightedAccuracy(
float goodWeight,
float badWeight
)
float GetBestWeightedAccuracy(
const EClassificationDataset& dataset
)
Parameters
goodWeight
-
badWeight
-
dataset
Dataset to get the label weight from.
Remarks
When using a dataset as the source for the label weights, the good weight is the weight of the "good" label and the bad weight is the sum of the weights of all the other labels.
EUnsupervisedSegmenterMetrics.GetBestWeightedAccuracy
Best achievable weighted accuracy.
The weighted accuracy is the weighted average of the true positive rate and the true negative rate (which is equal to 1 minus the false positive rate). See EROCPoint.
The classification threshold corresponding to this accuracy is given by EUnsupervisedSegmenterMetrics::GetBestWeightedAccuracyClassificationThreshold.
Namespace: Euresys.Open_eVision.EasyDeepLearning
[C#]
float GetBestWeightedAccuracy(
float goodWeight,
float badWeight
)
float GetBestWeightedAccuracy(
Euresys.Open_eVision.EasyDeepLearning.EClassificationDataset dataset
)
Parameters
goodWeight
-
badWeight
-
dataset
Dataset to get the label weight from.
Remarks
When using a dataset as the source for the label weights, the good weight is the weight of the "good" label and the bad weight is the sum of the weights of all the other labels.