19. The non-transitory computer-readable medium of claim 18 , wherein a categorization metric comprises an importance weight, and wherein the method further comprises: further in response to determining that the one or more metrics associated with performance of the trained document categorizer do not satisfy the first threshold, automatically: assigning respective importance weights to the electronic documents, the importance weights indicating an importance of the categorization of the respective electronic documents to the performance of the document categorizer.
18. A non-transitory computer-readable medium storing a set of instructions that, when executed by one or more processors, cause the one or more processors to perform a method of categorizing electronic documents, the method comprising: receiving categorizations for electronic documents included in a first seed set, the electronic documents in the first seed set being selected among a corpus of electronic documents; training a document categorizer on the categorizations using a machine learning algorithm; categorizing the remaining electronic documents in the corpus using the trained document categorizer; comparing one or more metrics associated with performance of the trained document categorizer to a first threshold associated with performance of the trained document categorizer, the one or more metrics being determined based on the categorizations; in response to determining that the one or more metrics associated with performance of the trained document categorizer do not satisfy the first threshold, automatically: analyzing a portion of electronic documents among the corpus different from the electronic documents included in the first seed set to identify one or more electronic documents of the portion that have been assigned respective categorization metrics satisfying or not satisfying a second threshold, wherein the second threshold is associated with categorizations metrics applicable to individual electronic documents; designating the one or more electronic documents of the portion as a second seed set; and providing the second seed set for categorization; receiving categorizations for the electronic documents included in the second seed set; retraining the document categorizer on the categorized electronic documents included in the second seed set using the machine learning algorithm; re-categorizing the remaining electronic documents in the corpus using the retrained document categorizer; comparing one or more metrics associated with performance of the retrained document categorizer to the first threshold, the one or more metrics being determined based on the re-categorizations of the remaining electronic documents; and iterating through generating seed sets, retraining the document categorizer, and re-categorizing the remaining electronic documents in the corpus using the retrained document categorizer until the one or more metrics associated with performance of the retrained document categorizer are greater than the first threshold.