Create and visualize random 3D vectors with anomalies:

Train an anomaly detector function on the training set:

Use the anomaly detector function to find and visualize the anomalous examples in the test set:

Change the anomaly detection false-positive rate by specifying the AcceptanceThreshold:

Train an anomaly detector on cat images using an image features FeatureExtractor:

Use the anomaly detector on cat and aircraft pictures:

This is equivalent to manually training your own FeatureExtractorFunction and using it as a preprocessor:

Train an anomaly detector and give each column a name:

Use the trained function on an association with the same keys:

Train an anomaly detector on integers, but treat them as categories instead of numbers:

Machine numbers will now be recognized as anomalies:

Obtain training and test datasets of images:

Add "out-of-distribution" examples to the test set:

Train the anomaly detector using the "Multinormal" method:

Find anomalous examples in the test set:

Train the anomaly detector using the "KernelDensityEstimation" method and attempt to find anomalies:

Load the Fisher's Irises dataset with its numerical attributes:

Train an anomaly detector function by specifying the PerformanceGoal:

Compare the training time for anomaly detector functions with different performance goals:

Specify the random seed that is used during training for repeatable results:

Test the trained anomaly detector on a range of values:

Check that you get the same result after training a new detector using the same RandomSeeding:

Train an anomaly detector using the GPU:

Obtain a dataset of images and train an anomaly detector function by specifying the time goal:

Obtain the training time of the anomaly detection:

Obtain a dataset of images:

Show training progress interactively without the plots:

Print the training progress periodically during training:

Show a simple progress indicator:

Specify a test dataset to validate the anomaly detector against during training:

The ValidationSet influences the trained distribution used to detect anomalies:

Specify the weights for the examples to make the trained detector more sensitive to certain values: