Predictive, machine-learning, time-series computer models suitable for sparse training sets

A tangible, non-transitory, machine-readable medium storing instructions that when executed by one or more processors effectuate operations comprising:

• obtaining, with one or more processors, for a plurality of entities, entity logs, wherein:
• the entity logs comprise events involving the entities,
• a first subset of the events are actions by the entities,
• at least some of the actions by the entities are targeted actions, and
• the events are labeled according to an ontology of events having a plurality of event types;
• transforming, with one or more processors, each entity log into a collection of features to which the predictive machine learning model is capable of responding;
• training, with one or more processors, based on the transformed collection of features from the entity logs, a predictive machine learning model to predict whether an entity characterized by a set of inputs to the model will engage in a targeted action in a given duration of time in the future;
• and
• storing, with one or more processors, the trained predictive machine learning model in memory.