Xgboost

PREDICTION OF INDUSTRIAL EQUIPMENT CONDITION USING COST-SENSITIVE APPROACHES AND CLASSIFICATION THRESHOLD OPTIMIZATION

This paper presents a comprehensive study on the application of modern machine learning methods for predictive maintenance based on the open AI4I Predictive Maintenance dataset. The primary goal of the research is to develop and compare both binary and multiclass classification models that enable not only the prediction of machine failures but also the identification of specific failure types.

Intellectual Analysis of Textual Data in Social Networks Using BERT and XGBoost

This article presents a comprehensive approach to sentiment analysis in social networks by leveraging modern text processing methods and machine learning algorithms. The primary focus is the integration of the Sentence-BERT model for text vectorization and XGBoost for sentiment classification. Using the Sentiment140 dataset, an extensive study of text messages labeled with sentiment annotations was conducted. The Sentence-BERT model enables the generation of high-quality vector representations of textual data, preserving both lexical and contextual relationships between words.

Research into machine learning algorithms for the construction of mathematical models of multimodal data classification problems

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.