Learn about regularized regression using python with housing price data retrieved from Kaggle /
This dataset guide provides a comprehensive overview of regression analysis, focusing on its fundamentals, prerequisites, and limitations. The guide requires students to use Google Collab for running illustrations provided in this guide. It delves into the necessity for regression model regularizati...
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| Format: | eBook |
| Language: | English |
| Published: |
London :
SAGE Publications Ltd,
2025.
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| Series: | SAGE research methods. Cases
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
| Summary: | This dataset guide provides a comprehensive overview of regression analysis, focusing on its fundamentals, prerequisites, and limitations. The guide requires students to use Google Collab for running illustrations provided in this guide. It delves into the necessity for regression model regularization to address issues such as overfitting and multicollinearity. The article explores two primary regularization techniques, L1 and L2 regularization, and their respective implementations in Lasso, Ridge, and Elastic Net models. Lasso regression, which uses L1 regularization, is highlighted for its ability to select features by shrinking some coefficients to zero. Ridge regression, employing L2 regularization, is discussed for its effectiveness in handling multicollinearity by penalizing large coefficients. Elastic Net, a hybrid approach that combines L1 and L2 penalties, is examined for its flexibility in managing the strengths of both Lasso and Ridge regressions.To illustrate the practical application of the regularization models discussed, the guide presents an example using a real dataset. This case study demonstrates how regularization can improve model performance by reducing overfitting and enhancing generalizability. Key insights from the example underscore the importance of selecting appropriate regularization techniques based on the specific characteristics of the data and the modeling objectives.The article also include section dedicated to additional resources, providing readers with further reading materials and tools for deepening their understanding of regression analysis and regularization techniques. These resources aim to support continued learning and application of advanced regression methods in various contexts of data science and statistical analysis. The dataset file is accompanied by a teaching guide, a student guide, and how-to guide. |
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| Physical Description: | 1 online resource : illustrations |
| ISBN: | 9781036209353 1036209350 |