Automatic Selection and Parameter Optimization of Mathematical Models Based on Machine Learning

Abstract

With the rapid progress of machine learning (ML) technology, more and more ML algorithms have emerged, and the complexity of models is also constantly increasing. This development trend brings two significant challenges in practice: how to choose appropriate algorithm models and how to optimize hyperparameters for these models. In this context, the concept of Automatic Machine Learning (AutoML) has emerged. Due to the applicability of different algorithm models to different data types and problem scenarios, it is crucial to automatically select the most suitable model based on the characteristics of specific tasks. AutoML integrates multiple ML algorithms and automatically filters based on the statistical characteristics of data and task requirements, aiming to provide users with the best model selection solution. Hyperparameters are parameters that ML models need to set before training, such as learning rate, number of iterations, regularization strength, etc., which have a significant impact on the performance of the model. AutoML integrates advanced hyperparameter optimization techniques to automatically find the optimal parameter combination, thereby improving the model's generalization ability and prediction accuracy. This article studies the automatic selection and parameter optimization of mathematical models based on ML.