Abstract
The work is devoted to the statistical analysis of the text and the study of the dynamics of classification. In the work, the selection of statistical features of the text, the classification of texts belonging to different authors, and the study of the dynamics of classification accuracy depending on the length of text fragments are carried out. To solve the problem, the following methods were used: natural language processing methods; statistical characteristics of texts; machine learning methods; dimensionality reduction methods for visualization capability. On the basis of the obtained dynamics of changes in classification accuracy depending on the lengths of text fragments, appropriate conclusions were drawn regarding the optimal length of texts used for training and testing models. The task was solved in the Jupyter Notebook software environment of the Anaconda distribution, which allows you to immediately install Python and the necessary libraries.
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