Machine-learning models to leverage behavior-dependent processes

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

obtaining, with a computer system, a first set of training data,

the first set of training data comprising a time-series of events that are caused by an actor entity, and

at least some events of the time-series of events comprising a plurality of attributes;

selecting, with the computer system, a plurality of subsets of the first set of training data, a first subset among the plurality of subsets representing a first interval of time and a second subset among the plurality of subsets representing a second interval of time after the first interval of time;

training, with the computer system, a first machine-learning model on the first set of training data by optimizing parameters of the first machine-learning model with a first objective function based on an accuracy of the first machine-learning model in predicting attributes of the second subset based on the attributes of the first subset;

generating, with the computer system, a virtual set of training data, comprising:

virtual events in a third interval after the first interval; and

virtual events in a fourth interval after the third interval;

training, with the computer system, a second machine-learning model on the virtual set of training data by optimizing parameters of the second machine-learning model with a second objective function based on an accuracy of the second machine-learning model in predicting attributes of the fourth subset based on the attributes of the third subset; and

storing, with the computer system, the trained second machine-learning model in memory.

2. The medium of claim 1 , wherein:

the first set of training data comprise a plurality of classifications; and

the first machine-learning model is trained based on the plurality of classifications.

3. The medium of claim 1 , wherein the generation of the virtual set of training data comprises repeatedly adding new time-series of events that are caused by the actor entity.

4. The medium of claim 1 , wherein:

the third subset comprises exogenous events.

5. The medium of claim 1 , the operations further comprising:

filtering attributes from the second subset that are identical to the attributes of the first subset.

6. The medium of claim 1 , wherein:

the third subset comprises events that are caused by other actor entities, wherein the events caused by other actor entities occurred before the third interval.

7. The medium of claim 1 , wherein:

obtaining the first set of training data further comprises filtering the events that occurred before a designated date from the time-series of events.

8. The medium of claim 1 , wherein:

at least a portion of the second interval overlaps with at least a portion of the third interval.

9. The medium of claim 1 , wherein:

the attributes of at least some events comprise a question and a response received from the actor entity.

10. The medium of claim 1 , wherein:

at least some events comprise offers presented to the actor entity and offers accepted by the actor entity.

11. The medium of claim 1 , the operations further comprising:

adjusting parameters of a value function indicative of an occurrence probability of an attribute in the fourth subset.

12. The medium of claim 11 , the operations further comprising:

scoring attributes of the fourth subset based on the occurrence probability calculated by the value function.

13. The medium of claim 12 , the operations further comprising:

generating sequences of future events based on the score of the attributes of the fourth subset.

14. The medium of claim 1 , wherein:

the first machine learning model is part of a value function or an environment model of a reinforcement learning model; and

the second trained machine learning model is a random decision forest model that includes a plurality of weighted trained decision trees.

15. The medium of claim 1 , wherein:

the first plurality of subsets comprises more than 100,000 different attributes;

the first or the second machine learning model executes on a compute cluster having a plurality of computing devices that collectively perform an in-memory cluster computing;

program state upon which the first or the second machine learning model operates is stored in an in-memory, immutable, distributed dataset spread over a plurality of nodes of the compute cluster such that the distributed dataset is resilient to failure of a given one of the computing devices; and

the compute cluster concurrently processes data in the distributed dataset to apply or train the first or the second machine learning model.

16. The medium of claim 1 , wherein:

training the first machine-learning model comprises steps for training a supervised time-series forecasting model; and

training the second machine-learning model comprises steps for training a supervised classification model.

17. The medium of claim 1 , wherein:

the operations comprise steps for causing at least some of the attributes of the fourth plurality of the subsets to respond to the actor entity in a targeted manner based on a trained model.

18. The medium of claim 1 , wherein:

the operations comprise steps for predicting probability of the attributes of the fourth plurality of the subsets related to the actor entity.

19. The medium of claim 1 , the operations further comprising:

adjusting, through a plurality of iterations, parameters of the second machine-learning model to increase the accuracy of the second machine-learning model in predicting the attributes of the fourth plurality of the subsets.

20. A method, comprising:

obtaining, with a computer system, a first set of training data,

the first set of training data comprising a time-series of events that are caused by an actor entity, and

at least some events of the time-series of events comprising a plurality of attributes;

selecting, with the computer system, a plurality of subsets of the first set of training data, a first subset among the plurality of subsets representing a first interval of time and a second subset among the plurality of subsets representing a second interval of time after the first interval of time;

training, with the computer system, a first machine-learning model on the first set of training data by optimizing parameters of the first machine-learning model with a first objective function based on an accuracy of the first machine-learning model in predicting attributes of the second subset based on the attributes of the first subset;

generating, with the computer system, a virtual set of training data, comprising:

virtual events in a third interval after the first interval; and

virtual events in a fourth interval after the third interval;

training, with the computer system, a second machine-learning model on the virtual set of training data by optimizing parameters of the second machine-learning model with a second objective function based on an accuracy of the second machine-learning model in predicting attributes of the fourth subset based on the attributes of the third subset; and

storing, with the computer system, the trained second machine-learning model in memory.

21. The medium of claim 1 , wherein:

training the first machine-learning model comprises steps for training a supervised classification model; and

training the second machine-learning model comprises steps for training a recommendation model.

22. The medium of claim 1 , the operations further comprising:

adjusting, through a plurality of iterations, parameters of the first machine-learning model to increase the accuracy of the second machine-learning model in predicting the attributes of the fourth plurality of the subsets.

23. The medium of claim 1 , wherein:

the second machine-learning model is implemented through a sequential pipeline of operations; and

the accuracy of the second machine-learning model is computed by applying an objective function to internal attributes of the pipeline of operations.

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

obtaining, with a computer system, a first set of training data,

the first set of training data comprising a time-series of events that are caused by an actor entity, and

at least some events of the time-series of events comprising a plurality of attributes;

selecting, with the computer system, a plurality of subsets of the first set of training data, a first subset among the plurality of subsets representing a first interval of time and a second subset among the plurality of subsets representing a second interval of time after the first interval of time;

training, with the computer system, a first machine-learning model on the first set of training data by optimizing parameters of the first machine-learning model with a first objective function based on a first performance metric of the first machine-learning model in predicting attributes of the second subset based on the attributes of the first sub set;

generating, with the computer system, a virtual set of training data, comprising:

virtual events in a third interval after the first interval, and

virtual events in a fourth interval after the third interval;

training, with the computer system, a second machine-learning model on the virtual set of training data by optimizing parameters of the second machine-learning model with a second objective function based on a second performance metric of the second machine-learning model in predicting attributes of the fourth subset based on the attributes of the third subset; and

storing, with the computer system, the trained second machine-learning model in memory.

25. The medium of claim 24 , wherein the first performance metric or the second performance metric is indicative of accuracy in prediction.