Comparison of different machine learning models: Linear model, forest and SVM

Over the past century, machine learning has developed rapidly with the fast evolution of AI. As high-performance computing (HPC) has popularized over the past few decades, huge differences have been made in the field of machine learning, from expert systems to deep learning algorithms nowadays. This study summarizes some well-known models and compares three of the current popular machine learning models: the Linear Model, the Forest Model, and the Supporting Vector Machine (SVM), which aims to give and clearer view of those models in several aspects. Forest and SVM are more powerful in performance than the Linear Model due to their ability to deal with more types and more complex relationships and patterns. However, Forest and SVM have a higher complexity than the Linear Model and are less interpretable. Through the comparison of the three models, the basic model and algorithms will be better understood, the appropriate model will be selected faster when solving the problem, saving training time and space, and effectively saving resources and costs.