Optimization and Improvement of Reliability of Machine Learning Algorithms Based on Regularization Methods

Nowadays, optimizing the results of machine learning algorithms is of great importance in the field of artificial intelligence. This article analyzes the effectiveness of Lasso, Ridge, and Elastic Net regression methods to increase the reliability of the results of machine learning algorithms in assessing economic indicators. These methods help to eliminate the problems of overfitting and multicollinearity, and are aimed at improving models based on machine learning algorithms. The study tests the effectiveness of these methods on a data set of economic indicators, and uses the Grid Search method to determine the optimal values for model parameters. As a result, it is shown that these approaches can improve the accuracy and generalizability of economic indicator assessments.