Capitalized Comparison of Three Machine Learning Models: Linear Model, Decision Tree, Neural Network

Abstract

As a matter of fact, with the rapid development of computation ability, machine learning has boosted rapidly with much faster training speed in recent years. In reality, machine learning is transforming industries with its ability to derive insights and patterns within massive datasets. Among numerous algorithms available, certain foundational models stand out due to their efficiency and capability. With this in mind, this study compares three typical models among various machine learning scenarios, i.e., linear models, decision trees, and neural network models. According to the analysis, the basic principle, concepts as well as parameters will be demonstrated. While all three has its unique pros and cons, this study aims to guide readers in choosing most fitting model with their tasks, by clarify the difference and future outlooks of these models. At the same time, the future development trends for the machine learning models will be proposed based on the analysis. Overall, these results shed light on guiding further exploration of machine learning.