An online system uses classifiers to predict the socioeconomic group of users of the online system. The classifiers use models that are trained using features based on global information about a population of users such as demographic information, device ownership, internet usage, household data, and socioeconomic status. The global information can be aggregated from market research questionnaires and provided to the online system. The classifiers input information about a user and output a probability that the user belongs to a given socioeconomic group. The input information is based on a user profile on the online system associated with the user as well as actions performed by the user on the online system. Thus, the online system can predict the user's socioeconomic group without using the user's income information. The online system can generate content for presentation to the user based on the predicted socioeconomic group.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: receiving user information about a target user of an online system, the user information describing a profile on the online system associated with the target user and describing interactions performed by the target user on the online system; retrieving socioeconomic information describing a plurality of socioeconomic groups; retrieving global information describing a plurality of users of the online system, each user associated with one of the socioeconomic groups; identifying, for each user of the plurality of users, features describing at least demographic data of the user, goods owned by the user, and a corresponding socioeconomic group of the user based on the global information; training one or more models using the identified features, the one or more models used by a plurality of classifiers, each classifier associated with a socioeconomic group of the plurality of socioeconomic groups and configured to determine a probability that the target user belongs to the socioeconomic group; and predicting, from the plurality of socioeconomic groups, a predicted socioeconomic group of the target user using the plurality of classifiers and the user information. 2. The method of claim 1, wherein the features further describe internet usage of the user, an education level of the user, and a travel history of the user. 3. The method of claim 1, further comprising: receiving a target socioeconomic group from a third party system outside of the online system; receiving sponsored content from the third party system; generating, in response to determining that the target socioeconomic group matches the predicted socioeconomic group of the target user, a content item based on the sponsored content; and providing the content item to a client device of the user. 4. The method of claim 1, wherein the global information is categorized based on a geographical location of each user of the plurality of users. 5. The method of claim 1, wherein the plurality of socioeconomic groups includes working class, middle class, and upper class. 6. The method of claim 1, wherein determining the predicted socioeconomic group of the target user comprises: determining, for each classifier of the plurality of classifiers, a probability that the target user is associated with a socioeconomic group corresponding to the classifier; and determining, in response to one of the probabilities being greater than a threshold confidence score, the predicted socioeconomic group of the target user. 7. The method of claim 6, wherein the threshold confidence score is adjustable based on the global information. 8. The method of claim 1, wherein the goods owned by the user describes internet connected client devices owned by the user or household appliances owned by the user. 9. The method of claim 1, further comprising: receiving the socioeconomic information; receiving the global information based on responses to a questionnaire; and storing the socioeconomic information and the global information. 10. The method of claim 1, wherein the one or more models include decision tree models, each decision tree model including a series of questions each corresponding to one of the identified features. 11. A computer program product comprising a non-transitory computer readable storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to: receive user information about a target user of an online system, the user information describing a profile on the online system associated with the target user and describing interactions performed by the target user on the online system; retrieve socioeconomic information describing a plurality of socioeconomic groups; retrieve global information describing a plurality of users of the online system, each user associated with one of the socioeconomic groups; identify, for each user of the plurality of users, features describing at least demographic data of the user, goods owned by the user, and a corresponding socioeconomic group of the user based on the global information; train a one or more models using the identified features, the one or more models used by a plurality of classifiers, each classifier associated with a socioeconomic group of the plurality of socioeconomic groups and configured to determine a probability that the target user belongs to the socioeconomic group; and predict, from the plurality of socioeconomic groups, a predicted socioeconomic group of the target user using the plurality of classifiers and the user information. 12. The non-transitory computer readable storage medium of claim 11, wherein the features further describe internet usage of the user, an education level of the user, and a travel history of the user. 13. The non-transitory computer readable storage medium of claim 11, having further instructions that when executed by the processor cause the processor to: receive a target socioeconomic group from a third party system outside of the online system; receive sponsored content from the third party system; generate, in response to determining that the target socioeconomic group matches the predicted socioeconomic group of the target user, a content item based on the sponsored content; and provide the content item to a client device of the user. 14. The non-transitory computer readable storage medium of claim 11, wherein the global information is categorized based on a geographical location of each user of the plurality of users. 15. The non-transitory computer readable storage medium of claim 11, wherein the plurality of socioeconomic groups includes working class, middle class, and upper class. 16. The non-transitory computer readable storage medium of claim 11, wherein determine the predicted socioeconomic group of the target user comprises: determine, for each classifier of the plurality of classifiers, a probability that the target user is associated with a socioeconomic group corresponding to the classifier; and determine, in response to one of the probabilities being greater than a threshold confidence score, the predicted socioeconomic group of the target user. 17. The non-transitory computer readable storage medium of claim 16, wherein the threshold confidence score is adjustable based on the global information. 18. The non-transitory computer readable storage medium of claim 11, wherein the goods owned by the user describes internet connected client devices owned by the user or household appliances owned by the user. 19. The non-transitory computer readable storage medium of claim 11, having further instructions that when executed by the processor cause the processor to: receive the socioeconomic information; receive the global information based on responses to a questionnaire; and store the socioeconomic information and the global information. 20. The non-transitory computer readable storage medium of claim 11, wherein the one or more models include decision tree models, each decision tree model including a series of questions each corresponding to one of the identified features.