Gaussian process on regression

Abstract

In this report, we discuss the application and usage of Gaussian Process in Classification and Regression. It is a flexible and powerful tool for modeling complex data. Thus, Gaussian process for classification and regression has risen in popularity in recent years. This report also provides an overview of Gaussian Process, its theory and formulas, and presents the different ways it can be used for classification and regression tasks through the different kernels. It also discusses the advantages and disadvantages of Gaussian Process compared to other popular common methods used in classification and regression. Lastly, the report also includes experiments to determine if Gaussian Process is effective in solving real-world classification and regression problems. Overall, the report highlights the potential of Gaussian processes as a useful tool for machine learning and data analysis and emphasizes how they can be an effective tool for data analysis and machine learning.