When collecting data, we split the dataset into training and testing sets. The model was trained with only the training set, and the testing set is used to validate how well the model will perform on un-seen data. This will ensure that the model has not learned to overfit the training data, which is a common occurrence.
To test your model, go to Model testing, and click Test all. The model will classify all of the test set samples and give you an overall accuracy of how your model performed.
This is also accompanied by a confusion matrix to show you how your models performs for each class.
To see a classification in detail, go to the individual sample you are want to evaluate and click the three dots next to it, then just select show classification. This will open a new window that will display the expected outcome, and the predicted output of your model with its accuracy. This detailed view can also give you a hint on why an item has been misclassified.
Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.
