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Hands-On Data Science with Anaconda

You're reading from   Hands-On Data Science with Anaconda Utilize the right mix of tools to create high-performance data science applications

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Length 364 pages
Edition 1st Edition
Languages
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Authors (2):
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James Yan James Yan
Author Profile Icon James Yan
James Yan
Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (15) Chapters Close

Preface 1. Ecosystem of Anaconda 2. Anaconda Installation FREE CHAPTER 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

A model's performance measure

In this chapter, we looked at several applications of linear models, including CAPM, the Fama-French 3-factor linear model, the Fama-French-Carhart 4-factor linear model, and the Fama-French 5-factor linear model. Obviously, CAPM is the simplest one since it only involves a market index as the explanatory variable. One question remains though: which model is the best? In other words, how do we rank these models and how is their performance measured? When running linear regressions, the output will show both the R2 and adjusted R2. When comparing models with different numbers of independent variables, the adjusted R2 is a better measure since it is adjusted by the number of input variables. However, note we should not depend only on the adjusted R2 since this is in the sample measure. In other words, a higher adjusted R2 simply means that based...

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