Various embodiments relating to partitioning a data set for training machine-learning classifiers based on an output of a globally trained machine-learning classifier are disclosed. In one embodiment, a first machine-learning classifier may be trained on a set of training data to produce a corresponding set of output data. The set of training data may be partitioned into a plurality of subsets based on the set of output data. Each subset may correspond to a different class. A second machine-learning classifier may be trained on the set of training data using a plurality of classes corresponding to the plurality of subsets to produce, for each data object of the set of training data, a probability distribution having for each class a probability that the data object is a member of the class.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing system comprising: a logic machine; a storage machine holding instructions executable by the logic machine to: train a first machine-learning classifier on a set of training data to produce a corresponding set of output data; partition the set of training data into a plurality of subsets based on the set of output data, each subset corresponding to a different class; and train a second machine-learning classifier on the set of training data using a plurality of classes corresponding to the plurality of subsets to produce, for each data object of the set of training data, a probability distribution having for each class a probability that the data object is a member of the class. 2. The computing system of claim 1, where the set of training data is unlabeled prior to being used to train the first machine-learning classifier, and the set of output data produced by the first machine-learning classifier includes, for each data object of the set of training data, a probability distribution determining a probability that the data object is a value within a range of observed values. 3. The computing system of claim 1, where the set of training data is labeled prior to being used to train the first machine-learning classifier such that the set of training data is classified into a first plurality of subsets corresponding to a first plurality of classes, and where the set of training data is partitioned based on the set of output data produced by the first machine-learning classifier to re-classify the set of training data into a second plurality of subsets different than the first plurality of subsets, the second plurality of subsets corresponding to a second plurality of classes different than the first plurality of classes. 4. The computing system of claim 3, where one or more subsets of the second plurality of subsets are determined based on the set of output data produced by the first machine-learning classifier including, for a number of data objects of the set of training data greater than a confusion threshold, a probability distribution indicating that the first machine-learning classifier is not able to distinguish between two or more classes of the first plurality of classes. 5. The computing system of claim 1, where the set of training data is partitioned into a learned number of subsets that is determined based on the set of output data produced by the first machine-learning classifier. 6. The computing system of claim 1, where the plurality of subsets are determined by applying a K-means clustering algorithm to the set of output data. 7. The computing system of claim 1, where the output data of the first machine-learning classifier includes one or more decision trees, and where the plurality of subsets are determined based on similarities of leaf nodes of the one or more decision trees. 8. The computing system of claim 1, where the storage machine further holds instructions executable by the logic machine to: for each subset, train a subset-specific machine-learning classifier on the subset of the training data to produce, for each data object of the subset, a probability distribution having for each class a probability that the data object is a member of the class. 9. The computing system of claim 1, where the storage machine further holds instructions executable by the logic machine to: receive real-world data to be classified; run the second machine-learning classifier on the real-world data to produce output data including a probability distribution having for each class a probability that the real-world data is a member of the class; and in response to the probability distribution indicating that the second machine-learning classifier is not able to distinguish between two or more classes, run the subset-specific machine-learning classifiers of the subsets corresponding to the two or more classes on the real-world data to produce output data including a probability distribution having for each class a probability that the real-world data is a member of the class. 10. The computing system of claim 9, where the storage machine further holds instructions executable by the logic machine to: in response to the probability distribution produced by the second machine-learning classifier indicating that the real-world data is a member of a class, assign the class to the real-world data; and in response to the probability distribution produced by the subset-specific machine-learning classifier indicating that the real-world data is a member of a class, assign the class to the real-world data. 11. A computing system comprising: a logic machine; a storage machine holding instructions executable by the logic machine to: receive real-world data to be classified; run a first machine-learning classifier on the real-world data to produce output data, the first machine-learning classifier being trained on a set of training data partitioned into a plurality of subsets corresponding to a plurality of classes, the output data including a probability distribution having for each class a probability that the real-world data is a member of the class; select a subset-specific machine-learning classifier based on the output data produced by the first machine-learning classifier, the subset-specific machine-learning classifier being trained on a subset of the set of training data; and run the subset-specific machine-learning classifier on the real-world data to produce output data including a probability distribution having for each class a probability that the real-world data is a member of the class. 12. The computing system of claim 11, where the storage machine further holds instructions executable by the logic machine to: in response to the probability distribution produced by the first machine-learning classifier indicating that the real-world data is a member of a class, assign the class to the real-world data; and in response to the probability distribution produced by the subset-specific machine-learning classifier indicating that the real-world data is a member of a class, assign the class to the real-world data. 13. The computing system of claim 11, where the subset-specific machine-learning classifier is selected from a plurality of subset-specific machine-learning classifiers based on the output data produced by the first machine-learning classifier, and where each of the plurality of subset-specific machine-learning classifiers are trained on a different subset of the plurality of subsets. 14. The computing system of claim 11, where the subset-specific machine-learning classifier is selected based on the probability distribution indicating that the first machine-learning classifier is not able to distinguish between two or more classes. 15. The computing system of claim 11, where the set of training data is partitioned into a learned number of subsets that is determined based on a set of output data produced by a machine-learning classifier. 16. The computing system of claim 15, where the plurality of subsets are determined by applying a K-means clustering algorithm to the set of output data. 17. The computing system of claim 11, where the output data of the first machine-learning classifier includes one or more decision trees, and where the plurality of partitions are determined based on similarities of leaf nodes of the one or more decision trees. 18. A method for classifying data comprising: receiving real-world data to be classified; running a first machine-learning classifier on the real-world data to produce output data, the first machine-learning classifier being trained on a set of training data partitioned into a plurality of subsets corresponding to a plurality of classes, the output data including a first probability distribution having for each class a probability that the real-world data is a member of the class; in response to the first probability distribution indicating that the first machine-learning classifier is not able to distinguish between two or more classes, select a subset-specific machine-learning classifier trained on a subset of training data that corresponds to the two or more classes; and run the subset-specific machine-learning classifier on the real-world data to produce output data including a second probability distribution having for each class a probability that the real-world data is a member of the class. 19. The method of claim 18, further comprising: in response to the first probability distribution produced by the first machine-learning classifier indicating that the real-world data is a member of a class, assigning the class to the real-world data; and in response to the second probability distribution produced by the subset-specific machine-learning classifier indicating that the real-world data is a member of a class, assigning the class to the real-world data. 20. The method of claim 18, further comprising: in response to the second probability distribution indicating that the subset-specific machine-learning classifier is not able to distinguish between two or more classes, select a different subset-specific machine-learning classifier trained on a different subset of training data that corresponds to the two or more classes; run the different subset-specific machine-learning classifier on the real-world data to produce output data including a third probability distribution having for each class a probability that the real-world data is a member of the class; and in response to the third probability distribution produced by the subset-specific machine-learning classifier indicating that the real-world data is a member of a class, assigning the class to the real-world data.