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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

Averaging

Averaging is usually used for regression problems or can be used while estimating the probabilities in classification tasks. Predictions are extracted from multiple models and an average of the predictions are used to make the final prediction.

Getting ready

Let us get ready to build multiple learners and see how to implement averaging:

Download the whitewines.csv dataset from GitHub and copy it to your working directory, and let's read the dataset:

df_winedata = pd.read_csv("whitewines.csv")

Let's take a look at the data with the following code:

df_winedata.head(5)

In the following screenshot, we can see that the data has been read properly:

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You have been reading a chapter from
Ensemble Machine Learning Cookbook
Published in: Jan 2019
Publisher: Packt
ISBN-13: 9781789136609
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