Various technologies described herein pertain to estimating predictive accuracy gain of a potential feature added to a set of features, wherein an existing predictor is trained on the set of features. Outputs of the existing predictor for instances in a dataset can be retrieved from a data store. Moreover, a predictive accuracy gain estimate of a potential feature added to the set of features can be measured as a function of the outputs of the existing predictor for the instances in the dataset. The predictive accuracy gain estimate can be measured without training an updated predictor on the set of features augmented by the potential feature.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A method executed by one or more computer processors, the method comprising: computing a predictive accuracy gain estimate of a potential feature, the predictive accuracy gain estimate indicative of a potential contribution if the potential feature were to be added to a set of features, the predictive accuracy gain estimate of the potential feature being computed based on outputs of an existing predictor for instances in a dataset, wherein: the existing predictor is trained on the set of features; the set of features on which the existing predictor is trained lacks the potential feature; and the predictive accuracy gain estimate is computed without training an updated predictor on the set of features augmented by the potential feature; and outputting the predictive accuracy gain estimate of the potential feature. 22. The method of claim 21, the predictive accuracy gain estimate of the potential feature further being computed based on labels for the instances in the dataset and values of the potential feature for the instances in the dataset, wherein the labels are target outputs being predicted for the instances in the dataset. 23. The method of claim 22, further comprising: training an incremental predictor based on the outputs of the existing predictor for the instances in the dataset, the labels for the instances in the dataset, and the values of the potential feature for the instances in the dataset; and evaluating performance of the incremental predictor to compute the predictive accuracy gain estimate of the potential feature. 24. The method of claim 23, wherein the incremental predictor is trained to predict proxy values of a loss gradient of the existing predictor for the instances in the dataset based on the values of the potential feature for the instances in the dataset. 25. The method of claim 24, wherein the proxy values of the loss gradient comprise values of a pseudo gradient. 26. The method of claim 24, wherein the proxy values of the loss gradient comprise values of a gradient estimate. 27. The method of claim 24, further comprising computing a correlation between outputs of the incremental predictor and the proxy values of the loss gradient. 28. The method of claim 23, wherein the incremental predictor is trained to predict values of a loss gradient of the existing predictor for the instances in the dataset based on the values of the potential features for the instances in the dataset. 29. The method of claim 21, wherein the predictive accuracy gain estimate of the potential feature is computed without access to the existing predictor. 30. The method of claim 21, wherein the predictive accuracy gain estimate of the potential feature is computed without use of values of features in the set of features on which the existing predictor is trained. 31. The method of claim 21, further comprising computing the predictive accuracy gain estimate of the potential feature based on a dependency of the potential feature to a residual of the existing predictor. 32. The method of claim 21, further comprising: computing a second predictive accuracy gain estimate of a second potential feature, the second predictive accuracy gain estimate indicative of a second potential contribution if the second potential feature were to be added to the set of features, the second predictive accuracy gain estimate of the second potential feature being computed based on the outputs of the existing predictor for the instances in the dataset, wherein: the set of features on which the existing predictor is trained lacks the second potential feature; and the second predictive accuracy gain estimate is computed without training an updated predictor on the set of features augmented by the second potential feature; and outputting the second predictive accuracy gain estimate of the second potential feature. 33. The method of claim 21, wherein the predictive accuracy gain estimate is computed for the potential feature and a second potential feature, the predictive accuracy gain estimate indicative of the potential contribution if the potential feature and the second potential feature were to be added to the set of features. 34. The method of claim 21, further comprising: computing a confidence score for whether the potential feature improves predictive accuracy; and outputting the confidence score. 35. A system that evaluates a predictive accuracy gain estimate of a potential feature, comprising: one or more processors; and data storage, comprising: outputs of an existing predictor for instances in a dataset, wherein the existing predictor is trained on a set of features that lacks the potential feature; labels for the instances in the dataset; values of the potential feature for the instances in the dataset; and components that are executable by the one or more processors, the components comprising: a learner component that trains an incremental predictor based on the outputs of the existing predictor for the instances in the dataset, the labels for the instances in the dataset, and the values of the potential feature for the instances in the dataset; and a measurement component that computes the predictive accuracy gain estimate of the potential feature based on outputs of the incremental predictor, the predictive accuracy gain estimate indicative of a potential contribution if the potential feature were to be added to the set of features. 36. The system of claim 35, wherein the measurement component computes the predictive accuracy gain estimate without an updated predictor being trained on the set of features augmented by the potential feature. 37. The system of claim 35, wherein the learner component trains the incremental predictor to predict proxy values of a loss gradient of the existing predictor for the instances in the dataset based on the values of the potential feature for the instances in the dataset. 38. The system of claim 35, wherein the measurement component computes the predictive accuracy gain estimate without access to the existing predictor. 39. The system of claim 35, the components further comprising a validation component that performs a test to determine a confidence score that signifies whether the potential feature improves a prediction of the existing predictor. 40. A system that evaluates a predictive accuracy gain estimate of a potential feature, comprising: one or more processors; and data storage, comprising: outputs of an existing predictor for instances in a dataset, wherein the existing predictor is trained on a set of features that lacks the potential feature; labels for the instances in the dataset; values of the potential feature for the instances in the dataset; and a measurement component that is executable by the one or more processors, the measurement component computes the predictive accuracy gain estimate of the potential feature based on the outputs of the existing predictor for the instances in the dataset, the labels for the instances in the dataset, and the values of the potential feature for the instances in the dataset, wherein the predictive accuracy gain estimate is measured without training an updated predictor on the set of features augmented by the potential feature.