Study of the Efficiency of Classification Methods in Forecasting in Machine Learning Tasks

Abstract

The article considers the use of classification methods to solve the problem of predicting the aerodynamic properties of materials. The methodology of classification by methods of machine learning is offered and investigated. The following logistic regression (LR), K-nearest neighbors (KNN) method, decision trees (DT) and random forest (RF) were used as classification methods. The methodology consists of the following stages: data collection, exploratory data analysis, modeling, evaluation of model efficiency, and improving model efficiency. To implement the forecasting procedure, preliminary data processing was performed, which consists of stages: Data collection and Intelligence data analysis. The next stage – Modeling, consists of two parts: Preparation and Selection of the model. The accuracy of forecasts is calculated. The analysis examined the prediction results in terms of accuracy, such as response, F-measure, Kappa, performance value (ROC) and error rate measured by the mean absolute error (MAE) and the root mean square error (RMSE). The analysis of forecasting accuracy is carried out.