How does Verity machine learning classify pages?

The objective of a supervised learning model is to predict the correct classification for newly presented page content, based on prior training data. 

Verity supervised machine learning works by first training a machine learning model with training data that comprises thousands of example pages for each content category paired with the correctly labeled outputs. For example, to learn how to threat classify a category on “Drugs and Alcohol,” first a human has to hand-annotate thousands of pieces of content that reference drugs or alcohol. 

The supervised learning algorithm searches for patterns in the data that correlate with the desired outputs. After training, the supervised learning algorithm can process new unseen pages and label them with a classification based on the prior training data. For example, the model could predict whether content is relevant to drugs or alcohol and classify it accordingly.