This paper presents a hybrid quantum logistic regression model for binary data classification that combines classical logistic regression methods with quantum computing to improve efficiency and speed up the learning process.
This paper addresses the challenge of automating toxic content detection within the Ukrainian segment of the Internet, a critical task given the scarcity of specialized linguistic resources for this language. The study focuses on developing and evaluating an information technology framework capable of effectively classifying toxic messages using foundational machine learning algorithms. For the experimental phase, a dataset comprising 4,600 records was compiled by aggregating data from YouTube and Google Play with existing open-source datasets.
Society now faces a serious issue from the spread of false information and fake news in news content. The detection and eradication of fake news have been made possible by machine learning techniques. This study examines an ensemble machine learning model's performance in identifying false information in news content. Five distinct machine learning methods are used in the study, including Naive Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Voting.
This paper investigates the application of machine learning methods for predicting the additional value of customer transactions aimed at improving the efficiency of commercial departments. The relevance of the study is driven by the rapid growth of customer data volumes and the need to transition from intuition-based decision-making to model-driven approaches.
This study explores an approach to improving the performance of logistic regression model (LR) integrated with Analytic Hierarchy Process (AHP) for weight initialization model with regularization and adaptation of gradient descent (GD).
This article discusses the practical aspects of applying logistic regression for binary data classification. Logistic regression determines the probability of an object belonging to one of two classes. This probability is calculated with the help of a sigmoid function, the argument of which is a linear convolution of the feature vector of the object with the weighting coefficients obtained during the minimization of the logarithmic loss function. Predicted class labels are determined by comparing the calculated probability with a given threshold value.
The article describes the results of research into the processes of complex analysis of military information based on machine learning and natural language processing to help commanders of tactical units. The system should allow users to have the following capabilities: combining the dictionary and information material, adding terms and abbreviations to the dictionary, classifying objects for radio technical intelligence, visualizing aerial objects, classifying aerial objects, using information materials, organizing information materials.
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.
This paper describes an information system for monitoring and analyzing reviews on social networks to form recommendations for the purchase of goods. This system is designed to be used by customers to speed up and facilitate the search for the necessary products on e-commerce resources. Successful selection of a quality product according to the desired criteria is extremely important, as it saves search time and customer money. Analyzing comments on the network, the information system recommends the product if there is a preponderance of positive feedback on it.