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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
Author (1):
Trent Hauck Trent Hauck
Profile icon Trent Hauck
Toc

Table of Contents (13) Chapters close

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

Creating sample data for toy analysis

If possible, use some of your own data for this book, but in the event you cannot, we'll learn how we can use scikit-learn to create toy data. scikit-learn's pseudo, theoretically constructed data is very interesting in its own right.

Getting ready

Very similar to getting built-in datasets, fetching new datasets, and creating sample datasets, the functions that are used follow the naming convention make_*. Just to be clear, this data is purely artificial:

from sklearn import datasets
datasets.make_*?

datasets.make_biclusters
datasets.make_blobs
datasets.make_checkerboard
datasets.make_circles
datasets.make_classification
...

To save typing, import the datasets module as d, and numpy...

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