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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. In a digital medium environment to predict user interaction with subsequent digital content, a method implemented by at least one computing device comprising: receiving training data, by the at least one computing device, that describes a first class of users having interactions with digital content and a second class of users that did not interact with the digital content; training a model using machine learning by the at least one computing device, the training including: sampling a subset of the training data from the first class and a subset of the training data from the second class; and iteratively selecting a batch from the sampled subsets of the training data from the first and second classes by the at least one computing device and iteratively processing the iteratively selected batch using machine learning by the at least one computing device to train the model; and predicting a likelihood of interaction with the subsequent digital content by the user using the trained model by the computing device. 2. The method as described in claim 1, wherein the machine learning is performed by the at least one computing device using a neural network. 3. The method as described in claim 1, wherein the batch is iteratively selected and iteratively processed using stochastic gradient descent (SGD). 4. The method as described in claim 1, wherein the training data is sampled iteratively by the at least one computing device as part of the training. 5. The method as described in claim 1, wherein: the first class is a positive class that describes the user interactions with the digital content that involve selection of the digital content or conversion of a related good or service; the second class is a negative class that describes users that did not select the digital content or convert the related good or service; and the first class has a fewer number of members than the second class. 6. The method as described in claim 1, wherein the training data is sampled based on a parameter that sets a sample size for the first or second classes. 7. The method as described in claim 1, wherein the predicted likelihood of interaction includes a probability that the subsequent digital content configured as an advertisement will be selected by the user or a probability of a likelihood of conversion of a related good or service by the user. 8. The method as described in claim 1, further comprising controlling subsequent user interaction with the subsequent digital content, by the at least one computing device, based at least in part on the predicted likelihood of interaction. 9. In a digital medium environment to generate a model to predict user interaction with subsequent digital content, an iterative machine learning system comprising: a sample selection module implemented at least partially in hardware to sample a subset of training data from a first class of users having interactions with digital content and a subset of the training data from a second class of users that did not interact with the digital content; a batch selection module configured to iteratively select a batch from the subset of the training data from the first class and the subset of the training data from the second class; and a classifier module configured to process the iteratively selected batch using machine learning to train the model to predict a likelihood of interaction with the subsequent digital content by the user. 10. The system as described in claim 9, wherein the batch selection module uses stochastic gradient descent (SGD) to iteratively select the batch. 11. The system as described in claim 9, wherein the sample selection module is configured to iteratively sample the subset of the training data from the first class and the subset of the training data from the second class that is used by the batch selection module to iteratively select the batch. 12. The system as described in claim 9, wherein: the first class is a positive class that describes the user interactions with the digital content that involve selection of the digital content or conversion of a related good or service; the second class is a negative class that describes users that did not select the digital content or convert the related good or service; and the first class has a fewer number of members than the second class. 13. The system as described in claim 9, wherein the sample selection module samples the training data based on a parameter that sets a sample size. 14. The system as described in claim 9, further comprising a prediction management module configured to predict a likelihood of interaction with the subsequent digital content by the user using the trained model. 15. The system as described in claim 14, wherein the predicted likelihood of interaction includes a probability that the subsequent digital content formed as an advertisement will be selected by the user or a probability of a likelihood of conversion of a related good or service by the user. 16. The system as described in claim 14, further comprising a service control module configured to control subsequent interaction with the subsequent digital content by the user based at least in part on the predicted likelihood of interaction. 17. In a digital medium environment to predict user interaction with subsequent digital content, a method implemented by at least one computing device comprising: receiving training data, by the at least one computing device that describes a first class of users having interactions with digital content and a second class of users that did not interact with the digital content; and training a model using a stochastic gradient decent technique as part of machine learning by the at least one computing device, the training including: iteratively sampling a subset of training data from the first class and a subset of the training data from the second class; and iteratively selecting a batch from the sampled subsets of the training data from the first and second classes by the at least one computing device and iteratively processing the selected batch using machine learning by the at least one computing device as part of the stochastic gradient descent technique to train the model to predict a likelihood of interaction with the digital content by the user. 18. The method as described in claim 16, further comprising predicting the likelihood of interaction with the digital content by the user using the trained model. 19. The system as described in claim 18, wherein the predicted likelihood of interaction includes: a probability that the subsequent digital content configured as an advertisement will be selected by the user; or a probability of a likelihood of conversion of a related good or service by the user through interaction with the subsequent digital content. 20. The system as described in claim 18, further comprising controlling subsequent user interaction with the subsequent digital content by the at least one computing device based at least in part on the predicted likelihood of interaction.