Evaluation of Text Materials Classification Quality Using «Random Forests» Machine Learning Algorithm
Abstract
The results of quality evaluation of text materials classification by the "random forests" machine learning algorithm implemented in the “scikit-learn” library are presented. Functions used in the “scikit-learn” library, as well as the parameters that affect classification quality, are described. The main stages of text materials classification are shown in the paper: the formation of sets of materials for training and control (ensuring sample representativeness, text processing, definition of groups for training and control); classifier model training; classifier model testing; quality evaluation of the obtained results. The quality evaluation is carried out using characteristics, such as precession, recall and F-measures of the classifier for various data preparation options: balanced and unbalanced training groups of materials, while the latter case is designed to convert the text into a set of tokens. Based on the results of the work, the main directions for improving quality of text materials classification by the "random forests" machine learning algorithm have been determined
DOI 10.14258/izvasu(2017)4-13
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