Methods, systems, and apparatus, including computer programs encoded on computer storage medium, for regularizing feature weights maintained by a machine learning model. The method includes actions of obtaining a set of training data that includes multiple training feature vectors, and training the machine learning model on each of the training feature vectors, comprising, for each feature vector and for each of a plurality of the features of the feature vector: determining a first loss for the feature vector with the feature, determining a second loss for the feature vector without the feature, and updating a current benefit score for the feature using the first loss and the second loss, wherein the benefit score for the feature is indicative of the usefulness of the feature in generating accurate predicted outcomes for training feature vectors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for regularizing a machine learning model that is configured to receive as input a feature vector that includes a plurality of features and to generate a predicted output from the feature vector, the method comprising: obtaining a set of training data that includes multiple training feature vectors; and training the machine learning model on each of the training feature vectors, comprising, for each feature vector: for each of a plurality of the features of the feature vector: determining a first loss for the feature vector with the feature; determining a second loss for the feature vector without the feature; and updating a current benefit score for the feature using the first loss and the second loss, wherein the benefit score for the feature is indicative of the usefulness of the feature in generating accurate predicted outcomes for training feature vectors. 2. The method of claim 1, wherein updating the current benefit score for the feature comprises determining a difference between the first loss and the second loss and updating the current benefit score using the difference. 3. The method of claim 1, wherein determining a first loss for the feature vector with the feature is based on the feature being associated with an unregularized feature weight for the feature that is determined in an immediately preceding training iteration. 4. The method of claim 1, wherein determining a second loss for the feature vector without the feature is based on the feature being associated with a weight that reduces the impact of the feature on the outcome generated by the machine learning model. 5. The method of claim 1, the method further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and in response to determining that the benefit score for a feature that satisfies a predetermined benefit threshold, scaling the unregularized weight associated with the feature based on the benefit score. 6. The method of claim 1, the method further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and in response to determining that the benefit score for a feature does not satisfy the predetermined benefit threshold, scaling the unregularized weight to a value that eliminates the feature from consideration by the machine learning model when making predictions. 7. The method of claim 1, the method further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and removing the feature from the learning model based on a determination that the benefit score did not satisfy the predetermined benefit threshold. 8. The method of claim 1, the method further comprising: ranking each of the features based on the benefit score associated with each respective feature. 9. The method of claim 8, the method further comprising: determining a predetermined number of features to include in the learning model; and selecting the predetermined number of features based on the ranking. 10. The method of claim 9, wherein determining a predetermined number of features to include in the learning model is based on the amount of available storage space to store the learning model. 11. The method of claim 1, wherein the machine learning model is an online learning model. 12. A system for regularizing a machine learning model that is configured to receive as input a feature vector that includes a plurality of features and to generate a predicted output from the feature vector, the system comprising: one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising: obtaining a set of training data that includes multiple training feature vectors; and training the machine learning model on each of the training feature vectors, comprising, for each feature vector: for each of a plurality of the features of the feature vector: determining a first loss for the feature vector with the feature; determining a second loss for the feature vector without the feature; and updating a current benefit score for the feature using the first loss and the second loss, wherein the benefit score for the feature is indicative of the usefulness of the feature in generating accurate predicted outcomes for training feature vectors. 13. The system of claim 12, wherein updating the current benefit score for the feature comprises determining a difference between the first loss and the second loss and updating the current benefit score using the difference. 14. The system of claim 12, wherein determining a first loss for the feature vector with the feature is based on the feature being associated with an unregularized feature weight for the feature that is determined in an immediately preceding training iteration. 15. The system of claim 12, wherein determining a second loss for the feature vector without the feature is based on the feature being scaled by a weight that reduces the impact of the feature on the outcome generated by the machine learning model. 16. The system of claim 12, the operations further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and in response to determining that the benefit score for a feature that satisfies a predetermined benefit threshold, scaling the unregularized weight associated with the feature based on the benefit score. 17. The system of claim 12, the operations further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and in response to determining that the benefit score for a feature does not satisfy the predetermined benefit threshold, scaling the unregularized weight to a value that eliminates the feature from consideration by the machine learning model when making predictions. 18. The system of claim 12, the operations further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and removing the feature from the learning model based on a determination that the benefit score did not satisfy the predetermined benefit threshold. 19. The system of claim 12, the operations further comprising: ranking each of the features based on the benefit score associated with each respective feature. 20. The system of claim 19, the operations further comprising: determining a predetermined number of features to include in the learning model; and selecting the predetermined number of features based on the ranking. 21. The system of claim 20, wherein determining a predetermined number of features to include in the learning model is based on the amount of available storage space to store the learning model. 22. The system of claim 12, wherein the machine learning model is an online learning model. 23. A non-transitory computer-readable medium storing software comprising instructions executable by one or more computers which, upon such execution, cause the one or more computers to perform operations for regularizing a machine learning model that is configured to receive as input a feature vector comprising a plurality of features and to generate a predicted output from the feature vector, the operations comprising: obtaining a set of training data that includes multiple training feature vectors; and training the machine learning model on each of the training feature vectors, comprising, for each feature vector: for each of a plurality of the features of the feature vector: determining a first loss for the feature vector with the feature; determining a second loss for the feature vector without the feature; and updating a current benefit score for the feature using the first loss and the second loss, wherein the benefit score for the feature is indicative of the usefulness of the feature in generating accurate predicted outcomes for training feature vectors. 24. The computer-readable medium of claim 23, wherein updating the current benefit score for the feature comprises determining a difference between the first loss and the second loss and updating the current benefit score using the difference. 25. The computer-readable medium of claim 23, wherein determining a first loss for the feature vector with the feature is based on the feature being associated with an unregularized feature weight for the feature that is determined in an immediately preceding training iteration. 26. The computer-readable medium of claim 23, wherein determining a second loss for the feature vector without the feature is based on the feature being scaled by a weight that reduces the impact of the feature on the outcome generated by the machine learning model. 27. The computer-readable medium of claim 23, the operations further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and in response to determining that the benefit score for a feature that satisfies a predetermined benefit threshold, scaling the unregularized weight associated with the feature based on the benefit score. 28. The computer-readable medium of claim 23, the operations further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and in response to determining that the benefit score for a feature does not satisfy the predetermined benefit threshold, scaling the unregularized weight to a value that eliminates the feature from consideration by the machine learning model when making predictions. 29. The computer-readable medium of claim 23, the operations further comprising: determining whether the benefit score for a feature satisfies a predetermined benefit threshold; and removing the feature from the learning model based on a determination that the benefit score did not satisfy the predetermined benefit threshold. 30. The computer-readable medium of claim 23, the operations further comprising: ranking each of the features based on the benefit score associated with each respective feature. 31. The computer-readable medium of claim 30, the operations further comprising: determining a predetermined number of features to include in the learning model; and selecting the predetermined number of features based on the ranking. 32. The computer-readable medium of claim 31, wherein determining a predetermined number of features to include in the learning model is based on the amount of available storage space to store the learning model. 33. The computer-readable medium of claim 23, wherein the machine learning model is an online learning model.