CPC G06F 18/217 (2023.01) [G06F 17/18 (2013.01); G06F 18/24 (2023.01); G06N 20/00 (2019.01)] | 17 Claims |
1. A method for data classification in a machine learning system, the method comprising:
generating, by a computing device, using training data a data classifier for a first dataset;
and
iterating, by the computing device, calibration of the data classifier based on a set of one or more parameters until an accuracy value for the data classifier matches or exceeds a predefined model accuracy threshold value, wherein the calibration comprises:
receiving a user input comprising an annotation of a presented subset of the first dataset to thereby disambiguate the presented subset of data, wherein the annotation comprises an indication of correctness;
generating using the disambiguated subset of data and the training data an enhanced version of the data classifier; and
determining the accuracy value based on an application of the enhanced version of the data classifier on at least another subset of the first dataset.
|