An online system simplifies modification of features used by machine learned models used by the online system, such as machined learned models with high dimensionality. The online system obtains a superset of features including features used by at least one machine learned model and may include additional features. From the superset of features, the online system generates various groups of features for a machine learned model. The groups of features may be a group including features currently used by the machine learned model, a group including all available features, and one or more intermediate groups. Intermediate groups include various numbers of features from the set selected based on measures of feature impact on the machine learned model associated with various features. A user may select a group of features, test the machine learning model using the selected group, and then launch the tested model based on the results.
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 a response based on the received features; determining a set of new features available for the model based on data maintained by the online system; determining a superset of features for the model, the superset of features comprising the plurality of features used by the model and the new features available for the model; determining measures of feature impact for one or more of the features in the superset of features, the measure of feature impact associated with a feature based at least in part on an effect of the feature on an accuracy of the model; generating a plurality of groups of features from the set of features based at least in part on the determined measures of feature impact, each group of features including one or more features differing from features included in the other groups of the plurality of groups; training the model using each of the plurality of groups of features, each group of features resulting in a performance metric for the model; and presenting information describing the performance metric of the model for each of the groups of features. 2. The method of claim 1, wherein generating the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact comprises: generating a group including one or more features received by an additional model, the additional model used concurrently with the model by the online system. 3. The method of claim 1, wherein generating the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact comprises: identifying a threshold measure of feature impact; and generating a group of features where each feature in the group has a measure of feature impact equaling or exceeding the threshold measure of feature impact. 4. The method of claim 1, wherein a group from the plurality of groups includes a predetermined number of the features having a highest measure of feature impact. 5. The method of claim 1, wherein generating the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact comprises: identifying a predetermined number of features having highest measures of feature impact; and generating a group including the identified predetermined number of features. 6. The method of claim 1, wherein generating the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact comprises: generating a group including each of the features in the superset of features; and generating an additional group including the plurality of features received by the model. 7. The method of claim 1, wherein training the model using each of the plurality of groups of features, each group of features resulting in a performance metric for the model comprises: determining whether the features of one or more of the plurality of groups of features are available for use by the model; and applying the model to groups of features determined to include features available for user by the model. 8. The method of claim 7, wherein applying the model to groups of features determined to include features available for user by the model comprises: presenting a user with an option to apply the model to one or more groups of features determined to include features available for user by the model; and applying the model to the one or more groups of features determined to include features available for user by the model in response to receiving a selection of the option. 9. The method of claim 7, wherein determining whether data associated with at least one of the identified one or more groups is available for use by the model comprises: determining whether at least a threshold amount of data is associated with each feature in an identified group; and determining data associated with features in the identified set of features is available for use by the model if at least the threshold amount of data is associated with each feature in the identified group. 10. The method of claim 7, wherein determining whether data associated with at least one of the identified one or more groups is available for use by the model comprises: determining whether at least a threshold amount of data is associated with each feature in an identified group; and determining data associated with features in the identified set of features is available for use by the model if at least the threshold amount of data is associated with each feature in the identified group. 11. The method of claim 7, wherein determining whether data associated with at least one of the identified one or more groups is available for use by the model comprises: determining whether data associated with at least a threshold number of features in a group are within a threshold amount of values associated with the features. 12. The method of claim 1, further comprising: receiving a selection of a group of features from the plurality of groups of features; and updating the model to use the selected group of features. 13. 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 a response based on the received features; determine a set of new features available for the model based on data maintained by the online system; determine a superset of features for the model, the superset of features comprising the plurality of features used by the model and the new features available for the model; determine measures of feature impact for one or more of the features in the superset of features, the measure of feature impact associated with a feature based at least in part on an effect of the feature on an accuracy of the model; generate a plurality of groups of features from the set of features based at least in part on the determined measures of feature impact, each group of features including one or more features differing from features included in the other groups of the plurality of groups; train the model using each of the plurality of groups of features, each group of features resulting in a performance metric for the model; and present information describing the performance metric of the model for each of the groups of features. 14. The computer program product of claim 13, wherein generate the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact comprises: identify a threshold measure of feature impact; and generate a group of features where each feature in the group has a measure of feature impact equaling or exceeding the threshold measure of feature impact. 15. The computer program product of claim 13, wherein generate the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact comprises: generate a group including one or more features received by an additional model, the additional model used concurrently with the model by the online system. 16. The computer program product of claim 13, wherein generate the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact comprises: generate a group including each of the features in the superset of features; and generate an additional group including the plurality of features received by the model. 17. The computer program product of claim 13, wherein train the model using each of the plurality of groups of features, each group of features resulting in a performance metric for the model comprises: determine whether the features of one or more of the plurality of groups of features are available for use by the model; and apply the model to groups of features determined to include features available for user by the model. 18. The computer program product of claim 17, wherein determine whether the features of one or more of the plurality of groups of features are available for use by the model comprises: determine whether at least a threshold amount of data is associated with each feature in an identified group; and determine data associated with features in the identified set of features is available for use by the model if at least the threshold amount of data is associated with each feature in the identified group. 19. The computer program product of claim 17, wherein determine whether the features of one or more of the plurality of groups of features are available for use by the model comprises: determine whether data associated with at least a threshold number of features in a group are within a threshold amount of values associated with the features. 20. A method comprising: maintaining a plurality of models at an online system, each model receiving a plurality of features and generating a response based on the received features; determining a set of new features available for a model from the plurality of models based on data maintained by the online system; determining a superset of features for the model, the superset of features comprising the plurality of features used by the model and the new features available for the model; determining measures of feature impact for one or more of the features in the superset of features, the measure of feature impact associated with a feature based at least in part on an effect of the feature on an accuracy of the model; determining measures of correlation between one or more features in the superset and additional features in the superset, the additional features used by an additional model from the plurality of models that concurrently used with the model by the online system; generating a plurality of groups of features from the set of features based at least in part on the determined measures of feature impact and the determined measures of correlation, each group of features including one or more features differing from features included in the other groups of the plurality of groups; training the model using each of the plurality of groups of features, each group of features resulting in a performance metric for the model; and presenting information describing the performance metric of the model for each of the groups of features. 21. The method of claim 20, wherein generating the plurality of groups of features from the set of features based at least in part on the determined measures of feature impact determined measures of feature impact and the determined measures of correlation comprises: identifying an additional feature having at least a threshold measure of correlation to a particular feature received by the model and having a measure of feature impact within a threshold amount of the particular feature received by the model; and generating a group including the identified additional feature and excluding the particular feature.