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scikit-learn Cookbook - Second Edition

You're reading from scikit-learn Cookbook - Second Edition

Product type Book

Published in Nov 2017

Publisher Packt

ISBN-13 9781787286382

Pages 374 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Author (1):

Trent Hauck

Table of Contents (13) Chapters

Preface

1. High-Performance Machine Learning – NumPy

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

Cross-validation with ShuffleSplit

The ShuffleSplit is one of the simplest cross-validation techniques. Using this cross-validation technique will simply take a sample of the data for the number of iterations specified.

Getting ready

The ShuffleSplit is a simple validation technique. We'll specify the total elements in the dataset, and it will take care of the rest. We'll walk through an example of estimating the mean of a univariate dataset. This is similar to resampling, but it'll illustrate why we want to use cross-validation while showing cross-validation.

How to do it...

...

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Authors (1)

Trent Hauck

Trent Hauck is a data scientist living and working in the Seattle area. He grew up in Wichita, Kansas and received his undergraduate and graduate degrees from the University of Kansas. He is the author of the book Instant Data Intensive Apps with pandas How-to, Packt Publishing—a book that can get you up to speed quickly with pandas and other associated technologies.

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