1. One or more hardware memory having embodied thereon computer-usable instructions that, when executed, facilitate a method of interactively labeling training data for machine learning, the method comprising: generating a first set of data items; alternating between repetitions of labeling sampled data items and repetitions of labeling searched data items; wherein labeling sampled data items includes A) providing a first set of data items, wherein each data item is scored with a probability of being a positive example of a particular class of data items, B) presenting a second set of one or more data items on a user interface, wherein the second set of data items is generated by sampling, from the first set of data items, one or more data items having scores lying within a range of scores selected to a) optimize precision of the classifier, wherein the scores selected to optimize precision of the classifier are greater than 0.5 on a scale of zero to one, or b) optimize recall of the classifier, C) receiving, via the user interface, one or more user-provided labels that identify one or more data items in the second set as positive or negative examples of the particular class of data items, and D) training the classifier based on the user-provided labels in the second set of data items; and wherein labeling searched data items includes A) presenting, on the user interface, a third set of one or more data items selected from the first set of data items based on a user-provided search query, B) receiving, via the user interface, one or more user-provided labels that identify one or more data items in the third set as positive or negative examples of the particular class of data items; and C) training the classifier based on the user-provided labels in the third set of data items.
1. One or more hardware memory having embodied thereon computer-usable instructions that, when executed, facilitate a method of interactively labeling training data for machine learning, the method comprising: generating a first set of data items; alternating between repetitions of labeling sampled data items and repetitions of labeling searched data items; wherein labeling sampled data items includes A) providing a first set of data items, wherein each data item is scored with a probability of being a positive example of a particular class of data items, B) presenting a second set of one or more data items on a user interface, wherein the second set of data items is generated by sampling, from the first set of data items, one or more data items having scores lying within a range of scores selected to a) optimize precision of the classifier, wherein the scores selected to optimize precision of the classifier are greater than 0.5 on a scale of zero to one, or b) optimize recall of the classifier, C) receiving, via the user interface, one or more user-provided labels that identify one or more data items in the second set as positive or negative examples of the particular class of data items, and D) training the classifier based on the user-provided labels in the second set of data items; and wherein labeling searched data items includes A) presenting, on the user interface, a third set of one or more data items selected from the first set of data items based on a user-provided search query, B) receiving, via the user interface, one or more user-provided labels that identify one or more data items in the third set as positive or negative examples of the particular class of data items; and C) training the classifier based on the user-provided labels in the third set of data items. 7. The one or more hardware memory of claim 1 , wherein the first set of data items includes data items randomly sampled from a larger set of data items.