An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: maintaining a model at an online system, the model receiving a plurality of features and generating an output based on the received features; identifying an additional feature for the model, a value for the additional feature based on values of one or more characteristics of one or more dimensions maintained by the online system at one or more times prior to a current time; obtaining data stored by the online system and including characteristics of one or more dimensions; generating a plurality of different partitions of the data stored by the online system based on the one or more dimensions, each partition of data including values of one or more characteristics of the one or more dimensions on which the additional feature is based; modifying each partition of data to include one or more values for the additional feature, a value of the additional feature included in a partition of data determined from values of one or more characteristics of the one or more dimensions on which the additional feature is based that are included in the partition of data and are associated with one or more times prior to the current time; generating a training set by combining the modified partitions of data; and applying a modified model including the additional feature to the training set to generate one or more results. 2. The method of claim 1, wherein modifying each partition of data to include one or more values for the additional feature based on characteristics of a content item associated with a partition of data comprises: modifying a plurality of partitions of data in parallel to include one or more values for the additional feature. 3. The method of claim 1, wherein a dimension is selected from a group consisting of: a user, a content item, and any combination thereof. 4. The method of claim 1, wherein modifying each partition of data to include one or more values for the additional feature based on characteristics of a content item associated with a partition of data comprises: modifying the partition of data to include a plurality of values for the additional feature, each value for the additional feature associated with a different time. 5. The method of claim 4, wherein a value for the additional feature associated with a time is based at least in part on one or more values of the additional feature associated with one or more time prior to the time. 6. The method of claim 1, wherein modifying each partition of data to include one or more values for the additional feature based on characteristics of the content item associated with the partition of data comprises: generating a value for the additional feature associated with a time by applying one or more additional models to values of one or more characteristics of the one or more dimensions on which the additional feature is based that are included in the partition of data and that are associated with times before the time; including the generated values in the partition of data. 7. The method of claim 6, wherein the one or more additional models account for one or more selected from a group consisting of: a decay rate, a propagation delay, and any combination thereof. 8. The method of claim 1, further comprising: applying the model to the training set to generate one or more alternative results; determining whether a difference between the one or more results and the one or more alternative results satisfies one or more criteria; and responsive to determining the difference between the one or more results satisfy the one or more criteria, replacing the model with the modified model. 9. A method comprising: maintaining a model at an online system, the model receiving a plurality of features and generating an output based on the received features; identifying an additional feature for the model, a value for the additional feature based on values of one or more characteristics of one or more dimensions maintained by the online system at one or more times prior to a current time; obtaining data stored by the online system and including characteristics of one or more dimensions; generating a plurality of different partitions of the data stored by the online system based on the one or more dimensions, each partition of data including values of one or more characteristics of the one or more dimensions on which the additional feature is based; modifying each partition of data to include one or more values for the additional feature, a value of the additional feature included in a partition of data determined from values of one or more characteristics of the one or more dimensions on which the additional feature is based that are included in the partition and are associated with one or more times prior to the current time; and generating a training set by combining the modified partitions of data. 10. The method of claim 9, wherein modifying each partition of data to include one or more values for the additional feature based on characteristics of a content item associated with a partition of data comprises: modifying a plurality of partitions of data in parallel to include one or more values for the additional feature. 11. The method of claim 9, wherein a dimension is selected from a group consisting of: a user, a content item, and any combination thereof. 12. The method of claim 9, wherein modifying each partition of data to include one or more values for the additional feature based on characteristics of a content item associated with a partition of data comprises: modifying the partition of data to include a plurality of values for the additional feature, each value for the additional feature associated with a different time. 13. The method of claim 12, wherein a value for the additional feature associated with a time is based at least in part on one or more values of the additional feature associated with one or more times prior to the time. 14. The method of claim 9, wherein modifying each partition of data to include one or more values for the additional feature based on characteristics of the content item associated with the partition of data comprises: generating a value for the additional feature associated with a time by applying one or more additional models to values of one or more characteristics of the one or more dimensions on which the additional feature is based that are included in the partition of data and that are associated with times before the time; including the generated values in the partition of data. 15. The method of claim 14, wherein the one or more additional models account for one or more selected from a group consisting of: a decay rate, a propagation delay, and any combination thereof. 16. The method of claim 9, wherein generating the training set by combining the modified portions of data comprises: generating the training set by combining a subset of the modified partitions of data. 17. A computer program product comprising a computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: maintain a model at an online system, the model receiving a plurality of features and generating an output based on the received features; identify an additional feature for the model, a value for the additional feature based on values of one or more characteristics of one or more dimensions maintained by the online system at one or more times prior to a current time; obtain data stored by the online system and including characteristics of one or more dimensions; generate a plurality of different partitions of the data stored by the online system based on the one or more dimensions, each partition of data including values of one or more characteristics of the one or more dimensions on which the additional feature is based; modify each partition of data to include one or more values for the additional feature, a value of the additional feature included in a partition of data determined from values of one or more characteristics of the one or more dimensions on which the additional feature is based that are included in the partition and are associated with one or more times prior to the current time; and generate a training set by combining the modified partitions of data. 18. The computer program product of claim 17, wherein a dimension is selected from a group consisting of: a user, a content item, and any combination thereof. 19. The computer program product of claim 17, wherein modify each partition of data to include one or more values for the additional feature based on characteristics of a content item associated with a partition of data comprises: modify the partition of data to include a plurality of values for the additional feature, each value for the additional feature associated with a different time. 20. The computer program product of claim 19, wherein a value for the additional feature associated with a time is based at least in part on one or more values of the additional feature associated with one or more times prior to the time.