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Ensemble Machine Learning Cookbook

You're reading from   Ensemble Machine Learning Cookbook Over 35 practical recipes to explore ensemble machine learning techniques using Python

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Length 336 pages
Edition 1st Edition
Languages
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Authors (2):
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Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Author Profile Icon Vijayalakshmi Natarajan
Vijayalakshmi Natarajan
Dipayan Sarkar Dipayan Sarkar
Author Profile Icon Dipayan Sarkar
Dipayan Sarkar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Get Closer to Your Data 2. Getting Started with Ensemble Machine Learning FREE CHAPTER 3. Resampling Methods 4. Statistical and Machine Learning Algorithms 5. Bag the Models with Bagging 6. When in Doubt, Use Random Forests 7. Boosting Model Performance with Boosting 8. Blend It with Stacking 9. Homogeneous Ensembles Using Keras 10. Heterogeneous Ensemble Classifiers Using H2O 11. Heterogeneous Ensemble for Text Classification Using NLP 12. Homogenous Ensemble for Multiclass Classification Using Keras 13. Other Books You May Enjoy

Resampling Methods

In this chapter, we will be introduced to the fundamental concept of sampling. We'll also learn about resampling and why it's important.

Sampling is the process of selecting a subset of observations from the population with the purpose of estimating some parameters about the whole population. Resampling methods, on the other hand, are used to improve the estimates of the population parameters.

In this chapter, we will cover the following recipes:

  • Introduction to sampling
  • k-fold and leave-one-out cross-validation
  • Bootstrap sampling
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