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Essential Statistics for Non-STEM Data Analysts

You're reading from   Essential Statistics for Non-STEM Data Analysts Get to grips with the statistics and math knowledge needed to enter the world of data science with Python

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781838984847
Length 392 pages
Edition 1st Edition
Languages
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Author (1):
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Rongpeng Li Rongpeng Li
Author Profile Icon Rongpeng Li
Rongpeng Li
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Getting Started with Statistics for Data Science
2. Chapter 1: Fundamentals of Data Collection, Cleaning, and Preprocessing FREE CHAPTER 3. Chapter 2: Essential Statistics for Data Assessment 4. Chapter 3: Visualization with Statistical Graphs 5. Section 2: Essentials of Statistical Analysis
6. Chapter 4: Sampling and Inferential Statistics 7. Chapter 5: Common Probability Distributions 8. Chapter 6: Parametric Estimation 9. Chapter 7: Statistical Hypothesis Testing 10. Section 3: Statistics for Machine Learning
11. Chapter 8: Statistics for Regression 12. Chapter 9: Statistics for Classification 13. Chapter 10: Statistics for Tree-Based Methods 14. Chapter 11: Statistics for Ensemble Methods 15. Section 4: Appendix
16. Chapter 12: A Collection of Best Practices 17. Chapter 13: Exercises and Projects 18. Other Books You May Enjoy

Chapter 11: Statistics for Ensemble Methods

In this chapter, we are going to investigate the ensemble method in terms of statistics and machine learning. The English word ensemble means a group of actors or musicians that work together as a whole. The ensemble method, or ensemble learning in machine learning, is not a specific machine learning algorithm, but a meta learning algorithm that builds on top of concrete machine learning algorithms to bundle them together to achieve better performance.

The ensemble method is not a single method, but a collection of many. In this chapter, we will cover the most important and representative ones.

We are going to cover the following in this chapter:

  • Revisiting bias, variance, and memorization
  • Understanding the bootstrapping and bagging techniques
  • Understanding and using the boosting module
  • Exploring random forests with scikit-learn

Let's get started!

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