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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

Fitting a line through data with machine learning

Linear regression with machine learning involves testing the linear regression algorithm on unseen data. Here, we will perform 10-fold cross-validation:

Split the set into 10 parts
Train on 9 of the parts and test on the one left over
Repeat this 10 times so that every part gets to be a test set once

Getting ready

As in the previous section, load the dataset you want to apply linear regression to, in this case, the Boston housing dataset:

from sklearn import datasets
boston = datasets.load_boston()

How to do it...

The...

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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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