Linear regression in machine learning

Yilu Wu

doi:10.1117/12.2628053

22 April 2022 Linear regression in machine learning

Yilu Wu

Proceedings Volume 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021); 121634T (2022) https://doi.org/10.1117/12.2628053
Event: International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 2021, Nanjing, China

Abstract

Machine learning is the study of how to make computers learn better from historical data, to produce an excellent model that can improve the performance of a system. It is widely used to solve complex problems in practical engineering applications, business analysis, other fields. With the development of technology, machine learning based on statistics has attracted people's attention and has been successfully applied in the fields of health care, technology commerce and other aspects. Machine learning estimates the dependence relationship between data based on known samples, to predict and judge unknown or unmeasurable data. It can provide decision-makers with reference opinions and help make better decisions. In the field of economics, it helps merchants to set prices and explore the impact of price changes on sales. This paper describes various Supervised Machine Learning classification techniques, compares various supervised learning algorithms as well as determines the most efficient classification algorithm based on the data set, the number of instances and variables (features). A simple linear regression model is used to study the relationship between salary and years of experience, and python is used to estimate the linear equation.

Citation Download Citation

Yilu Wu "Linear regression in machine learning", Proc. SPIE 12163, International Conference on Statistics, Applied Mathematics, and Computing Science (CSAMCS 2021), 121634T (22 April 2022); https://doi.org/10.1117/12.2628053

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