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Trent Hauck - Scikit-Learn Cookbook

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Trent Hauck Scikit-Learn Cookbook
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About This Book Learn how to handle a variety of tasks with Scikit-Learn with interesting recipes that show you how the library really works Use Scikit-Learn to simplify the programming side data so you can focus on thinking Discover how to apply algorithms in a variety of situationsWho This Book Is ForIf youre a data scientist already familiar with Python but not Scikit-Learn, or are familiar with other programming languages like R and want to take the plunge with the gold standard of Python machine learning libraries, then this is the book for you.What You Will Learn Address algorithms of various levels of complexity and learn how to analyze data at the same time Handle common data problems such as feature extraction and missing data Understand how to evaluate your models against themselves and any other model Discover just enough math needed to learn how to think about the connections between various algorithms Customize the machine learning algorithm to fit your problem, and learn how to modify it when the situation calls for it Incorporate other packages from the Python ecosystem to munge and visualize your datasetIn DetailPython is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. Its consistent API and plethora of features help solve any machine learning problem it comes across.The book starts by walking through different methods to prepare your databe it a dataset with missing values or text columns that require the categories to be turned into indicator variables. After the data is ready, youll learn different techniques aligned with different objectivesbe it a dataset with known outcomes such as sales by state, or more complicated problems such as clustering similar customers. Finally, youll learn how to polish your algorithm to ensure that its both accurate and resilient to new datasets.

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Trent Hauck - Scikit-Learn Cookbook
Scikit-Learn Cookbook
Trent Hauck
Scikit-Learn Cookbook

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scikit-learn Cookbook
scikit-learn Cookbook

Copyright 2014 Packt Publishing

All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.

Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.

Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.

First published: November 2014

Production reference: 1271014

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78398-948-5

www.packtpub.com

Credits

Author

Trent Hauck

Reviewers

Anoop Thomas Mathew

Xingzhong

Commissioning Editor

Kunal Parikh

Acquisition Editor

Owen Roberts

Content Development Editor

Dayan Hyames

Technical Editors

Mrunal M. Chavan

Dennis John

Copy Editors

Janbal Dharmaraj

Sayanee Mukherjee

Project Coordinator

Harshal Ved

Proofreaders

Simran Bhogal

Bridget Braund

Amy Johnson

Indexer

Tejal Soni

Graphics

Sheetal Aute

Ronak Dhruv

Abhinash Sahu

Production Coordinator

Manu Joseph

Cover Work

Manu Joseph

About the Author

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.

First, a big thanks to the Python software community, the people behind scikit-learn in particular; the skill with which the code is developed is responsible for a lot of good work that gets done.

Personally, I'd like to thank my family, friends, and coworkers.

About the Reviewers

Anoop Thomas Mathew is a software architect with years of experience in working with Python and software development in general. With the title of Chief Technology Officer at Profoundis Inc., he leads the engineering efforts at Profoundis and is now focusing on https://vibeapp.co. He has spoken at conferences such as The Fifth Elephant 2012, PyCon 2012, FOSSMeet 2013, PyCon 2013, and FOSSMeet 2014 to name a few. He blogs at http://infiniteloop.in.

He is the author of the book, Code Explorer's Guide to the Open Source Jungle , available online at https://leanpub.com/opensourcebook.

To my beloved.

Xingzhong is a PhD candidate in Electrical Engineering at Stevens Institute of Technology, Hoboken, New Jersey, where he works as a research assistant, designing and implementing machine-learning models in computer vision and signal processing applications.

Although Python is his primary programming language, occasionally, for fun and curiosity, his works might be written on golang, Scala, JavaScript, and so on. As a self-confessed technology geek, he is passionate about exploring new software and hardware.

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Preface

This book is designed in the same way that many data science and analytics projects play out. First, we need to acquire data; the data is often messy, incomplete, or not correct in some way. Therefore, we spend the first chapter talking about strategies for dealing with bad data and ways to deal with other problems that arise from data. For example, what happens if we have too many features? How do we handle that? The first chapter is your guide. The meat of the book will walk you through various algorithms and how to implement them into your workflow. And finally, we'll end with the postmodel workflow. This chapter is fairly agnostic to the other chapters and can be applied to the various algorithms you'll learn up until the final chapter.

What this book covers

, Premodel Workflow , walks you through the preparatory step of preparing a dataset for modeling and shows how scikit-learn can help to ameliorate the burden of preprocessing.

, Working with Linear Models , discusses how many problems can be viewed as linear models upon the appropriate application of a transformation, and therefore walks you through what may be the most used class of models.

, Building Models with Distance Metrics , encompasses a large number of topics that largely work by measuring the similarity between the data points. Because similarity and distance are often synonymous, clustering can often be used as long as a distance function can be defined.

, Classifying Data with scikit-learn , focuses on the various methods within scikit-learn that are used to determine a data point as some member between 1 and N classes.

, Postmodel Workflow , teaches us how we can take a basic model produced from one of the recipes and tune it so that we can achieve better results than we could with the basic model.

What you need for this book

Here are the contents of the requirements.txt file that will get the environment set up. This will allow you to follow along with the code in the book.

I've also included a conda requirements file; this method may be easier for less-experienced Python developers:

dateutil==2.1ipython==2.2.0ipython-notebook==2.1.0jinja2==2.7.3markupsafe==0.18matplotlib==1.3.1numpy==1.8.1patsy==0.3.0pandas==0.14.1pip==1.5.6pydot==1.0.28pyparsing==1.5.6pytz==2014.4pyzmq==14.3.1scikit-learn==0.15.0scipy==0.14.0setuptools==3.6six==1.7.3ssl_match_hostname==3.4.0.2tornado==3.2.2
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