Analysis of the Hyperparameter Selection in Machine Learning

Abstract. A Machine learning is now playing a role in various fields. With the development of the internet, the volume of data is increasing, and the algorithms of machine learning are becoming more complex. Adjusting hyperparameters is a challenge for beginners who are new to machine learning. This research implemented a simple neural network model through code. Moreover, this study has changed some of the most basic parameters of the model and trained different models. This study has used visualization methods to show how the model training time and performance change with the alteration of hyperparameters, and derived their respective accuracy rates. This article provides entry-level data and code for newcomers to the field of machine learning. Since this experiment uses a federated learning model, employs local_update SGD, and also utilizes parallel methods, it is quite comprehensive, covering many algorithm codes for machine learning beginners. Testing the basic parameters of the model can help newcomers to machine learning understand more clearly the impact of hyperparameters on the model. It also aids in the development of more algorithms for adjusting hyperparameters.