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scikit-learn Cookbook , Second Edition

You're reading from   scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Length 374 pages
Edition 2nd Edition
Languages
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Authors (2):
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Trent Hauck Trent Hauck
Author Profile Icon Trent Hauck
Trent Hauck
Julian Avila Julian Avila
Author Profile Icon Julian Avila
Julian Avila
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Table of Contents (13) Chapters Close

Preface 1. High-Performance Machine Learning – NumPy FREE CHAPTER 2. Pre-Model Workflow and Pre-Processing 3. Dimensionality Reduction 4. Linear Models with scikit-learn 5. Linear Models – Logistic Regression 6. Building Models with Distance Metrics 7. Cross-Validation and Post-Model Workflow 8. Support Vector Machines 9. Tree Algorithms and Ensembles 10. Text and Multiclass Classification with scikit-learn 11. Neural Networks 12. Create a Simple Estimator

Working with categorical variables

Categorical variables are a problem. On one hand they provide valuable information; on the other hand, it's probably text—either the actual text or integers corresponding to the text—such as an index in a lookup table.

So, we clearly need to represent our text as integers for the model's sake, but we can't just use the id field or naively represent them. This is because we need to avoid a similar problem to the Creating binary features through thresholding recipe. If we treat data that is continuous, it must be interpreted as continuous.

Getting ready

The Boston dataset won't be useful for this section. While it's useful for feature binarization, it...

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