activelearning: A Collection of Active Learning Methods in R
In ramhiser/activelearning: A Collection of Active Learning Methods in R

Active learning is a machine learning paradigm for optimally choosing unlabeled observations in a training data set to query for their true labels. The framework is particularly useful when there are very few labeled observations relative to a large number of unlabeled observations, and the user seeks to determine as few true labels as possible to achieve highly accurate classifiers. This package is a collection of various active learning methods from the literature to optimally query observations with respect to a variety of objective functions. Some active learning methods require posterior probability estimates of the unlabeled observations from a single classifier or a committee of classifiers; this package allows the user to specify custom classifiers. An excellent literature survey has been provided by Dr. Burr Settles.

ramhiser/activelearning documentation built on May 26, 2019, 10:06 p.m.