Digital content interaction prediction and training that addresses imbalanced classes

Abstract

Digital content interaction prediction and training techniques that address imbalanced classes are described. In one or more implementations, a digital medium environment is described to predict user interaction with digital content that addresses an imbalance of numbers included in first and second classes in training data used to train a model using machine learning. The training data is received that describes the first class and the second class. A model is trained using machine learning. The training includes sampling the training data to include at least one subset of the training data from the first class and at least one subset of the training data from the second class. Iterative selections are made of a batch from the sampled training data. The iteratively selected batches are iteratively processed by a classifier implemented using machine learning to train the model.