本文主要是介绍理解difference between Regression Analysis and Machine Learning，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
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Overview
查看Regression Analysis的定义时发现，Regression analysis is a set of statistical processes for estimating the relationship between a dependent variable and one or more independent variables.
那这不就是机器学习嘛？
再看一下Machine Learning的定义，Machine Learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data.
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Regression Versus Classification ML
从某个角度，regression是ML中的一个子类, or one of the ML “algorithms”.
Regression and Classification are categorized under the same umbrella of supervised machine learning.
The main difference between them is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete).
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Statistics and Machine Learning
The major difference between machine learning and statistics is their purpose.
Machine Learning models are designed to make the most accurate predictions possible.
Statistical models are designed for inference about the relationships between variabels.
Statistics is the mathematical study of data; you can’t do statistics unless you have data;
Statistical model is a model for the data that is used either to infer something about the relationship within the data or to create a model that is able to predict feature values.
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References
- Is Regression Analysis Really Machine Learning?
- Regression Versus Classification Machine Learning: What’s the Difference?
- The Actual Difference Between Statistics and Machine Learning
- Statistics versus machine learning
- When should linear regression be called “machine learning”?
- Regression vs. Classification in Machine Learning
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