Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций
Конференция: IX International Scientific Conference on Agricultural Science 2024 "Current State, Problems and Prospects for the Development of Agricultural Science" (AGRICULTURAL SCIENCE 2024) ; Simferopol, Crimea; Simferopol, Crimea
Год издания: 2024
Идентификатор DOI: 10.1051/bioconf/202414104050
Аннотация: <jats:p>The study employed several key data analysis methods aimed at enhancing the understanding of relationships between variables and improving prediction accuracy. The primary tool used was correlation analysis, which allowed for the identification of the degree of association between two variables by determining how changes inПоказать полностьюone variable relate to changes in another. This established a foundation for further in-depth data analysis. For a deeper understanding and simplified interpretation of the data, factor analysis was utilized. This method helped to identify latent factors that explain the relationships between observed variables and to reduce the number of variables by grouping them. This made the analysis easier and facilitated the identification of key components affecting the data. Logistic regression was applied to build data models. This method is used to model the probability of a specific event occurring based on independent variables, allowing for the classification and prediction of categorical outcomes. The logistic function was used to estimate probabilities and the relationship between the dependent variable and predictors. To enhance the performance of the logistic regression model, a Weight of Evidence (WoE) analysis was conducted. This method converts categorical and continuous variables into numerical formats, simplifying data interpretation and improving the model's predictive capabilities. WoE analysis helps to identify significant factors, improve the linear relationship between predictors and the dependent variable, and reduce the impact of outliers, which is particularly important in areas such as credit scoring. The results of applying these methods showed that the model based on correlation and factor analysis explained 27.51% of the information on the training set and 76.04% on the test set.</jats:p>
Журнал: BIO Web of Conferences
Выпуск журнала: 141
Номера страниц: 04050
Место издания: Les Ulis