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Python Machine Learning

You're reading from  Python Machine Learning

Product type Book
Published in Sep 2015
Publisher Packt
ISBN-13 9781783555130
Pages 454 pages
Edition 1st Edition
Languages
Author (1):
Sebastian Raschka Sebastian Raschka
Profile icon Sebastian Raschka

Table of Contents (21) Chapters

Python Machine Learning
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Giving Computers the Ability to Learn from Data 2. Training Machine Learning Algorithms for Classification 3. A Tour of Machine Learning Classifiers Using Scikit-learn 4. Building Good Training Sets – Data Preprocessing 5. Compressing Data via Dimensionality Reduction 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning 7. Combining Different Models for Ensemble Learning 8. Applying Machine Learning to Sentiment Analysis 9. Embedding a Machine Learning Model into a Web Application 10. Predicting Continuous Target Variables with Regression Analysis 11. Working with Unlabeled Data – Clustering Analysis 12. Training Artificial Neural Networks for Image Recognition 13. Parallelizing Neural Network Training with Theano Index

Bagging – building an ensemble of classifiers from bootstrap samples


Bagging is an ensemble learning technique that is closely related to the MajorityVoteClassifier that we implemented in the previous section, as illustrated in the following diagram:

However, instead of using the same training set to fit the individual classifiers in the ensemble, we draw bootstrap samples (random samples with replacement) from the initial training set, which is why bagging is also known as bootstrap aggregating. To provide a more concrete example of how bootstrapping works, let's consider the example shown in the following figure. Here, we have seven different training instances (denoted as indices 1-7) that are sampled randomly with replacement in each round of bagging. Each bootstrap sample is then used to fit a classifier , which is most typically an unpruned decision tree:

Bagging is also related to the random forest classifier that we introduced in Chapter 3, A Tour of Machine Learning Classifiers Using...

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