A Practical Significant Technic in Solving Overfitting: Regularization

Research Article
Open access

A Practical Significant Technic in Solving Overfitting: Regularization

Muyuan Li 1*
  • 1 Zhengzhou No.47 Middle &High School, Ping An Avenue No.6, Zhengzhou, Henan, China    
  • *corresponding author muyuanli528@gmail.com
TNS Vol.5
ISSN (Print): 2753-8826
ISSN (Online): 2753-8818
ISBN (Print): 978-1-915371-53-9
ISBN (Online): 978-1-915371-54-6

Abstract

The passage mainly discusses the solution to overfitting. Overfitting usually happens when people are training their machine learning models. When a model is overfitted, it only fits one particular dataset and misses most of the data points from another dataset. This problem affects the model's performance and makes it unable to use for its purpose. So how to solve this problem with significance and practical meaning? At the beginning of the passage, I will introduce some theoretical foundations for overfitting. Then I will define the concept of overfitting and show an example of overfitting in the machine learning model. After that, I will tell you how to pick the correct model with the testing set. Then, the passage focuses on the discussion of regularization, which is a helpful technique for solving overfitting. And I will compare the L1 and l2 regularization to help you find the suitable one.

Keywords:

Machine Learning Model, Overfitting, Regularization, L1 Norm, L2 Norm.

Li,M. (2023). A Practical Significant Technic in Solving Overfitting: Regularization. Theoretical and Natural Science,5,253-258.
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References

[1]. Catbug88@home: ~$. Introduction to Linear Algebra for Applied Machine Learning with Python. (n.d.). Retrieved October 23, 2022, from https://pabloinsente.github.io/intro-linear-algebra.

[2]. Serrano, L. G., & Thrun, S. (2021). Grokking Machine Learning. Manning Publications Co.

[3]. Tyagi, N. (n.d.). L2 vs L1 regularization in machine learning: Ridge and Lasso regularization. L2 vs L1 Regularization in Machine Learning | Ridge and Lasso Regularization. Retrieved October 23, 2022, from https://www.analyticssteps.com/blogs/l2-and-l1-regularization-machine-learning

Cite this article

Li,M. (2023). A Practical Significant Technic in Solving Overfitting: Regularization. Theoretical and Natural Science,5,253-258.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)

ISBN：978-1-915371-53-9(Print) / 978-1-915371-54-6(Online)
Editor：Marwan Omar, Roman Bauer
Conference website: https://www.confciap.org/
Conference date: 25 March 2023
Series: Theoretical and Natural Science
Volume number: Vol.5
ISSN：2753-8818(Print) / 2753-8826(Online)

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References

[1]. Catbug88@home: ~$. Introduction to Linear Algebra for Applied Machine Learning with Python. (n.d.). Retrieved October 23, 2022, from https://pabloinsente.github.io/intro-linear-algebra.

[2]. Serrano, L. G., & Thrun, S. (2021). Grokking Machine Learning. Manning Publications Co.

[3]. Tyagi, N. (n.d.). L2 vs L1 regularization in machine learning: Ridge and Lasso regularization. L2 vs L1 Regularization in Machine Learning | Ridge and Lasso Regularization. Retrieved October 23, 2022, from https://www.analyticssteps.com/blogs/l2-and-l1-regularization-machine-learning

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