This article reviews the theoretical aspects of logistic regression for binary data classification, including data preparation processes, training, testing, and model evaluation metrics.
Requirements for input data sets are formulated, methods of coding categorical data are described, methods of scaling input features are defined and substantiated.
In this article, the mathematical justification of logistic regression as an effective and simple to implement method of machine learning is performed.
A review of literary sources was conducted in the direction of statistical processing, analysis and classification of data using the logistic regression method, which confirmed the popularity of this method in various subject areas.
Currently, machine learning algorithms (ML) are increasingly integrated into everyday life. There are many areas of modern life where classification methods are already used. Methods taking into account previous predictions and errors that are calculated as a result of data integration to obtain forecasts for obtaining the classification result are investigated. A general overview of classification methods is conducted. Experiments on machine learning algorithms for multimodal data are performed.