Examples relate to providing hierarchical classifiers. In some examples, a superclass classifier of a hierarchy of classifiers is trained with a first type of prediction threshold, where the superclass classifier classifies data into one of a number of subclasses. At this stage, a subclass classifier is trained with a second type of prediction threshold, where the subclass classifier classifies the data into one of a number of classes. The first type of prediction threshold of the superclass classifier and the second type of prediction threshold of the subclass classifier are alternatively applied to classify data segments.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computing device for providing hierarchical classifiers, comprising: a memory to store a hierarchy of classifiers, wherein each level of the hierarchy of classifiers is associated with one of a plurality of prediction thresholds; a processor that: trains a superclass classifier of the hierarchy of classifiers with a first type of prediction threshold, wherein the superclass classifier classifies data into one of a plurality of subclasses; trains a subclass classifier of the plurality of subclasses with a second type of prediction threshold, wherein the subclass classifier classifies the data into one of a plurality of classes; and alternatively applies the first type of prediction threshold of the superclass classifier and the second type of prediction threshold of the subclass classifier to classify each of a plurality of data segments. 2. The computing device of claim 1, wherein the first type of prediction threshold is a recall threshold, a precision threshold, a fall-out threshold, a miss rate threshold, a negative predictive values (NPV) threshold, a specificity threshold, an accuracy threshold, an equal-error rate threshold, or an F1 score threshold and the second type of prediction threshold is the recall threshold, the precision threshold, the fall-out threshold, the miss rate threshold, the NPV threshold, the specificity threshold, the accuracy threshold, the equal-error rate threshold, or the F-score threshold, and wherein the first type of prediction threshold is different from the second type of prediction threshold. 3. The computing device of claim 2, wherein the recall threshold is used to minimize false negatives, and wherein the precision threshold is used to minimize false positives, and wherein the fall-out threshold is used to minimize true negatives, and wherein the miss rate threshold is used to minimize true positives, and wherein the NPV threshold is used to minimize false negatives, and wherein the specificity threshold is used to minimize false positives, and wherein the accuracy threshold is used to minimize false positives and false negatives, and wherein the equal-error rate threshold is used to maximize precision and recall, and wherein the F-score threshold is used to maximize precision and recall. 4. The computing device of claim 1, wherein the processor applies test data to the hierarchy of classifiers to determine if the first type of prediction threshold and the second type of prediction threshold are satisfied. 5. The computing device of claim 4, wherein the processor applies cross-validation data to the hierarchy of classifiers to determine F-scores for each classifier of the hierarchy of classifiers. 6. The computing device of claim 1, wherein the processor receives a plurality of selections specifying the first type of prediction threshold and the second type of prediction threshold from an administrator. 7. A method for providing hierarchical classifiers, comprising: receiving a plurality of selections specifying a first type of prediction threshold for a superclass classifier and a second type of prediction threshold for a subclass classifier from an administrator, wherein the superclass classifier and the subclass classifier are arranged in a hierarchy of classifiers; training the superclass classifier with the first type of prediction threshold, wherein the superclass classifier classifies data into one of a plurality of subclasses; training the subclass classifier of the plurality of subclasses with the second type of prediction threshold, wherein the subclass classifier classifies the data into one of a plurality of classes; and alternatively applying the first type of prediction threshold of the superclass classifier and the second type of prediction threshold of the subclass classifier to classify each of a plurality of observed data segments. 8. The method of claim 7, wherein the first type of prediction threshold is a recall threshold, a precision threshold, a fall-out threshold, a miss rate threshold, a negative predictive values (NPV) threshold, a specificity threshold, an accuracy threshold, an equal-error rate threshold, or an F1 score threshold and the second type of prediction threshold is the recall threshold, the precision threshold, the fall-out threshold, the miss rate threshold, the NPV threshold, the specificity threshold, the accuracy threshold, the equal-error rate threshold, or the F-score threshold, and wherein the first type of prediction threshold is different from the second type of prediction threshold. 9. The method of claim 8, wherein the recall threshold is used to minimize false negatives, and wherein the precision threshold is used to minimize false positives, and wherein the fall-out threshold is used to minimize true negatives, and wherein the miss rate threshold is used to minimize true positives, and wherein the NPV threshold is used to minimize false negatives, and wherein the specificity threshold is used to minimize false positives, and wherein the accuracy threshold is used to minimize false positives and false negatives, and wherein the equal-error rate threshold is used to maximize precision and recall, and wherein the F-score threshold is used to maximize precision and recall 10. The method of claim 7, further comprising applying test data to the hierarchy of classifiers to determine if the first type of prediction threshold and the second type of prediction threshold are satisfied. 11. The method of claim 10, further comprising applying cross-validation data to the hierarchy of classifiers to determine F-scores for each classifier of the hierarchy of classifiers. 12. A non-transitory machine-readable storage medium encoded with instructions executable by a processor for providing hierarchical classifiers, the machine-readable storage medium comprising instructions that: train the superclass classifier with the first type of prediction threshold that is a recall threshold, a precision threshold, a fall-out threshold, a miss rate threshold, a negative predictive values (NPV) threshold, a specificity threshold, an accuracy threshold, an equal-error rate threshold, or an F1 score threshold and the second type of prediction threshold is the recall threshold, the precision threshold, the fall-out threshold, the miss rate threshold, the NPV threshold, the specificity threshold, the accuracy threshold, the equal-error rate threshold, or the F-score threshold, wherein the superclass classifier classifies data into one of a plurality of subclasses; train the subclass classifier of the plurality of subclasses with the second type of prediction threshold that is the recall threshold, the precision threshold, the fall-out threshold, the miss rate threshold, the NPV threshold, the specificity threshold, the accuracy threshold, the equal-error rate threshold, or the F-score threshold, wherein the subclass classifier classifies the data into one of a plurality of classes; and alternatively apply the first type of prediction threshold of the superclass classifier and the second type of prediction threshold of the subclass classifier to classify each of a plurality of observed data segments. 13. The non-transitory machine-readable storage medium of claim 12, wherein the first type of prediction threshold is different from the second type of prediction threshold. 14. The non-transitory machine-readable storage medium of claim 13, wherein the recall threshold is used to minimize false negatives, and wherein the precision threshold is used to minimize false positives, and wherein the fall-out threshold is used to minimize true negatives, and wherein the miss rate threshold is used to minimize true positives, and wherein the NPV threshold is used to minimize false negatives, and wherein the specificity threshold is used to minimize false positives, and wherein the accuracy threshold is used to minimize false positives and false negatives, and wherein the equal-error rate threshold is used to maximize precision and recall, and wherein the F-score threshold is used to maximize precision and recall 15. The non-transitory machine-readable storage medium of claim 12, wherein the instructions apply test data to the hierarchy of classifiers to determine if the first type of prediction threshold and the second type of prediction threshold are satisfied.