The output of ML Attribute Generate consists of packed primitives. Each embedded geometry is a copy of the input geometry with specific attributes set to random values drawn from a probability distribution. See Machine Learning documentation for more general information.

ML Attribute Generate allows you to generate a set of random inputs, which can be turned into a set of labeled examples using a procedural network and the node ML Example. These labeled examples can then be written to disk using ML Example Output, trained on using ML Regression Train, resulting in an ML model that approximates the procedural network. This model can be applied using ML Regression Inference.