Ensemble Methods of Determining the Effective Activity of Enterprises

Ensemble methods, which are effective for determining the activities of enterprises, have been studied. They are based on the idea that a combination of several models can provide more accurate predictions than any single model. The application of ensemble methods allowed to provide more accurate and reliable results, which helps enterprises to make better decisions and improve their efficiency. The main types of ensemble methods include random forest, which is one of the most popular ensemble methods, which uses multiple tree solutions to provide more accurate results. Each tree is built on a subset of data and a subset of features; gradient boosting also uses multiple models to provide more accurate predictions. In this method, each subsequent model tries to correct the errors of the previous model, which leads to an improvement in the quality of forecasts; stacking uses multiple models of different types to provide better predictions. In this method, the results of several models are used as indications for another model, which allows to improvement of the quality of forecasting; bagging uses multiple copies of the same model to analyze the data and provide more accurate results. Each model is built on a random subset of data and a random subset of features. The study also used individual models that were combined into an ensemble. For example, linear regression and decision trees to get better results. Ensemble methods have been applied to determine enterprise performance in many industries, such as finance, marketing, and manufacturing. In this publication, an example of the implementation of ensemble methods on a dataset of enterprise functioning has been developed.