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Python Machine Learning Cookbook, - Second Edition

You're reading from  Python Machine Learning Cookbook, - Second Edition

Product type Book
Published in Mar 2019
Publisher Packt
ISBN-13 9781789808452
Pages 642 pages
Edition 2nd Edition
Languages
Authors (2):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
Prateek Joshi Prateek Joshi
Profile icon Prateek Joshi
View More author details
Toc

Table of Contents (18) Chapters close

Preface 1. The Realm of Supervised Learning 2. Constructing a Classifier 3. Predictive Modeling 4. Clustering with Unsupervised Learning 5. Visualizing Data 6. Building Recommendation Engines 7. Analyzing Text Data 8. Speech Recognition 9. Dissecting Time Series and Sequential Data 10. Analyzing Image Content 11. Biometric Face Recognition 12. Reinforcement Learning Techniques 13. Deep Neural Networks 14. Unsupervised Representation Learning 15. Automated Machine Learning and Transfer Learning 16. Unlocking Production Issues 17. Other Books You May Enjoy

Working with the Seaborn library

A box plot, also called a whiskers chart, is a graph that uses simple dispersion and position indexes to describe the distribution of a sample. A box plot can be depicted either horizontally or vertically, by means of a rectangular partition divided by two segments. The rectangle (box) is delimited by the first quartile (the 25th percentile) and the third quartile (the 75th percentile), and is divided by the median (the 50th percentile).

Getting ready

In this recipe, we will draw a box plot to show the distribution of the predictors contained in the Boston dataset, which we already used in the Estimating housing prices recipe in Chapter 1, The Realm of Supervised Learning.

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