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Unformatted text preview: Introduction to Machine Learning with Python A GUIDE FOR DATA SCIENTISTS powered by Andreas C. Müller & Sarah Guido Introduction to Machine Learning with Python A Guide for Data Scientists Andreas C. Müller and Sarah Guido Beijing Boston Farnham Sebastopol Tokyo Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido Copyright © 2017 Sarah Guido, Andreas Müller. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles ( ). For more information, contact our corporate/insti‐ tutional sales department: 800-998-9938 or [email protected] Editor: Dawn Schanafelt Production Editor: Kristen Brown Copyeditor: Rachel Head Proofreader: Jasmine Kwityn October 2016: Indexer: Judy McConville Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest First Edition Revision History for the First Edition 2016-09-22: 2017-01-13: 2017-06-09: First Release Second Release Third Release See for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Introduction to Machine Learning with Python, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights. 978-1-449-36941-5 [LSI] Table of Contents Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii 1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Why Machine Learning? Problems Machine Learning Can Solve Knowing Your Task and Knowing Your Data Why Python? scikit-learn Installing scikit-learn Essential Libraries and Tools Jupyter Notebook NumPy SciPy matplotlib pandas mglearn Python 2 Versus Python 3 Versions Used in this Book A First Application: Classifying Iris Species Meet the Data Measuring Success: Training and Testing Data First Things First: Look at Your Data Building Your First Model: k-Nearest Neighbors Making Predictions Evaluating the Model Summary and Outlook 1 2 4 5 5 6 7 7 7 8 9 10 11 12 12 13 15 17 19 21 22 23 23 iii 2. Supervised Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Classification and Regression Generalization, Overfitting, and Underfitting Relation of Model Complexity to Dataset Size Supervised Machine Learning Algorithms Some Sample Datasets k-Nearest Neighbors Linear Models Naive Bayes Classifiers Decision Trees Ensembles of Decision Trees Kernelized Support Vector Machines Neural Networks (Deep Learning) Uncertainty Estimates from Classifiers The Decision Function Predicting Probabilities Uncertainty in Multiclass Classification Summary and Outlook 27 28 31 31 32 37 47 70 72 85 94 106 121 122 124 126 129 3. Unsupervised Learning and Preprocessing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 Types of Unsupervised Learning Challenges in Unsupervised Learning Preprocessing and Scaling Different Kinds of Preprocessing Applying Data Transformations Scaling Training and Test Data the Same Way The Effect of Preprocessing on Supervised Learning Dimensionality Reduction, Feature Extraction, and Manifold Learning Principal Component Analysis (PCA) Non-Negative Matrix Factorization (NMF) Manifold Learning with t-SNE Clustering k-Means Clustering Agglomerative Clustering DBSCAN Comparing and Evaluating Clustering Algorithms Summary of Clustering Methods Summary and Outlook 133 134 134 135 136 138 140 142 142 158 165 170 170 184 189 193 209 210 4. Representing Data and Engineering Features. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Categorical Variables One-Hot-Encoding (Dummy Variables) iv | Table of Contents 214 215 Numbers Can Encode Categoricals Binning, Discretization, Linear Models, and Trees Interactions and Polynomials Univariate Nonlinear Transformations Automatic Feature Selection Univariate Statistics Model-Based Feature Selection Iterative Feature Selection Utilizing Expert Knowledge Summary and Outlook 220 222 226 234 238 238 240 242 244 252 5. Model Evaluation and Improvement. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253 Cross-Validation Cross-Validation in scikit-learn Benefits of Cross-Validation Stratified k-Fold Cross-Validation and Other Strategies Grid Search Simple Grid Search The Danger of Overfitting the Parameters and the Validation Set Grid Search with Cross-Validation Evaluation Metrics and Scoring Keep the End Goal in Mind Metrics for Binary Classification Metrics for Multiclass Classification Regression Metrics Using Evaluation Metrics in Model Selection Summary and Outlook 254 255 256 256 262 263 263 265 277 277 278 298 301 302 304 6. Algorithm Chains and Pipelines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307 Parameter Selection with Preprocessing Building Pipelines Using Pipelines in Grid Searches The General Pipeline Interface Convenient Pipeline Creation with make_pipeline Accessing Step Attributes Accessing Attributes in a Pipeline inside GridSearchCV Grid-Searching Preprocessing Steps and Model Parameters Grid-Searching Which Model To Use Summary and Outlook 308 310 311 314 315 316 317 319 321 322 7. Working with Text Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 325 Types of Data Represented as Strings 325 Table of Contents | v Example Application: Sentiment Analysis of Movie Reviews Representing Text Data as a Bag of Words Applying Bag-of-Words to a Toy Dataset Bag-of-Words for Movie Reviews Stopwords Rescaling the Data with tf–idf Investigating Model Coefficients Bag-of-Words with More Than One Word (n-Grams) Advanced Tokenization, Stemming, and Lemmatization Topic Modeling and Document Clustering Latent Dirichlet Allocation Summary and Outlook 327 329 331 332 337 338 340 341 346 349 350 357 8. Wrapping Up. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359 Approaching a Machine Learning Problem Humans in the Loop From Prototype to Production Testing Production Systems Building Your Own Estimator Where to Go from Here Theory Other Machine Learning Frameworks and Packages Ranking, Recommender Systems, and Other Kinds of Learning Probabilistic Modeling, Inference, and Probabilistic Programming Neural Networks Scaling to Larger Datasets Honing Your Skills Conclusion 359 360 361 361 362 363 363 364 365 365 366 366 367 368 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 vi | Table of Contents Preface Machine learning is an integral part of many commercial applications and research projects today, in areas ranging from medical diagnosis and treatment to finding your friends on social networks. Many people think that machine learning can only be applied by large companies with extensive research teams. In this book, we want to show you how easy it can be to build machine learning solutions yourself, and how to best go about it. With the knowledge in this book, you can build your own system for finding out how people feel on Twitter, or making predictions about global warming. The applications of machine learning are endless and, with the amount of data avail‐ able today, mostly limited by your imagination. Who Should Read This Book This book is for current and aspiring machine learning practitioners looking to implement solutions to real-world machine learning problems. This is an introduc‐ tory book requiring no previous knowledge of machine learning or artificial intelli‐ gence (AI). We focus on using Python and the scikit-learn library, and work through all the steps to create a successful machine learning application. The meth‐ ods we introduce will be helpful for scientists and researchers, as well as data scien‐ tists working on commercial applications. You will get the most out of the book if you are somewhat familiar with Python and the NumPy and matplotlib libraries. We made a conscious effort not to focus too much on the math, but rather on the practical aspects of using machine learning algorithms. As mathematics (probability theory, in particular) is the foundation upon which machine learning is built, we won’t go into the analysis of the algorithms in great detail. If you are interested in the mathematics of machine learning algorithms, we recommend the book The Elements of Statistical Learning (Springer) by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, which is available for free at the authors’ website. We will also not describe how to write machine learning algorithms from scratch, and will instead focus on vii how to use the large array of models already implemented in scikit-learn and other libraries. Why We Wrote This Book There are many books on machine learning and AI. However, all of them are meant for graduate students or PhD students in computer science, and they’re full of advanced mathematics. This is in stark contrast with how machine learning is being used, as a commodity tool in research and commercial applications. Today, applying machine learning does not require a PhD. However, there are few resources out there that fully cover all the important aspects of implementing machine learning in prac‐ tice, without requiring you to take advanced math courses. We hope this book will help people who want to apply machine learning without reading up on years’ worth of calculus, linear algebra, and probability theory. Navigating This Book This book is organized roughly as follows: • Chapter 1 introduces the fundamental concepts of machine learning and its applications, and describes the setup we will be using throughout the book. • Chapters 2 and 3 describe the actual machine learning algorithms that are most widely used in practice, and discuss their advantages and shortcomings. • Chapter 4 discusses the importance of how we represent data that is processed by machine learning, and what aspects of the data to pay attention to. • Chapter 5 covers advanced methods for model evaluation and parameter tuning, with a particular focus on cross-validation and grid search. • Chapter 6 explains the concept of pipelines for chaining models and encapsulat‐ ing your workflow. • Chapter 7 shows how to apply the methods described in earlier chapters to text data, and introduces some text-specific processing techniques. • Chapter 8 offers a high-level overview, and includes references to more advanced topics. While Chapters 2 and 3 provide the actual algorithms, understanding all of these algorithms might not be necessary for a beginner. If you need to build a machine learning system ASAP, we suggest starting with Chapter 1 and the opening sections of Chapter 2, which introduce all the core concepts. You can then skip to “Summary and Outlook” on page 129 in Chapter 2, which includes a list of all the supervised models that we cover. Choose the model that best fits your needs and flip back to read the viii | Preface section devoted to it for details. Then you can use the techniques in Chapter 5 to eval‐ uate and tune your model. Online Resources While studying this book, definitely refer to the scikit-learn website for more indepth documentation of the classes and functions, and many examples. There is also a video course created by Andreas Müller, “Advanced Machine Learning with scikitlearn,” that supplements this book. You can find it at advanced_machine_learning_scikit-learn. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. Constant width Used for program listings, as well as within paragraphs to refer to program ele‐ ments such as variable or function names, databases, data types, environment variables, statements, and keywords. Also used for commands and module and package names. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values deter‐ mined by context. This element signifies a tip or suggestion. This element signifies a general note. Preface | ix This icon indicates a warning or caution. Using Code Examples Supplemental material (code examples, IPython notebooks, etc.) is available for download at . This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a signifi‐ cant amount of example code from this book into your product’s documentation does require permission. We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “An Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido (O’Reilly). Copyright 2017 Sarah Guido and Andreas Müller, 978-1-449-36941-5.” If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at [email protected] O’Reilly Safari Safari (formerly Safari Books Online) is a membership-based training and reference platform for enterprise, government, educators, and individuals. Members have access to thousands of books, training videos, Learning Paths, interac‐ tive tutorials, and curated playlists from over 250 publishers, including O’Reilly Media, Harvard Business Review, Prentice Hall Professional, Addison-Wesley Profes‐ sional, Microsoft Press, Sams, Que, Peachpit Press, Adobe, Focal Press, Cisco Press, John Wiley & Sons, Syngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT Press, Apress, Manning, New Riders, McGraw-Hill, Jones & Bartlett, and Course Technology, among others. For more information, please visit . x | Preface How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-998-9938 (in the United States or Canada) 707-829-0515 (international or local) 707-829-0104 (fax) We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at . To comment or ask technical questions about this book, send email to bookques‐ [email protected] For more information about our books, courses, conferences, and news, see our web‐ site at . Find us on Facebook: Follow us on Twitter: Watch us on YouTube: Acknowledgments From Andreas Without the help and support of a large group of people, this book would never have existed. I would like to thank the editors, Meghan Blanchette, Brian MacDonald, and in par‐ ticular Dawn Schanafelt, for helping Sarah and me make this book a reality. I want to thank my reviewers, Thomas Caswell, Olivier Grisel, Stefan van der Walt, and John Myles White, who took the time to read the early versions of this book and provided me with invaluable feedback—in addition to being some of the corner‐ stones of the scientific open source ecosystem. I am forever thankful for the welcoming open source scientific Python community, especially the contributors to scikit-learn. Without the support and help from this community, in particular from Gael Varoquaux, Alex Gramfort, and Olivier Grisel, I would never have become a core contributor to scikit-learn or learned to under‐ stand this package as well as I do now. My thanks also go out to all the other contrib‐ utors who donate their time to improve and maintain this package. Preface | xi I’m also thankful for the discussions with many of my colleagues and peers that hel‐ ped me understand the challenges of machine learning and gave me ideas for struc‐ turing a textbook. Among the people I talk to about machine learning, I specifically want to thank Brian McFee, Daniela Huttenkoppen, Joel Nothman, Gilles Louppe, Hugo Bowne-Anderson, Sven Kreis, Alice Zheng, Kyunghyun Cho, Pablo Baberas, and Dan Cervone. My thanks also go out to Rachel Rakov, who was an eager beta tester and proofreader of an early version of this book, and helped me shape it in many ways. On the personal side, I want to thank my parents, Harald and Margot, and my sister, Miriam, for their continuing support and encouragement. I also want to thank the many people in my life whose love and friendship gave me the energy and support to undertake such a challenging task. From Sarah I would like to thank Meg Blanchette, without whose help and guidance this project would not have even existed. Thanks to Celia La and Brian Carlson for reading in the early days. Thanks to the O’Reilly folks for their endless patience. And finally, thanks to DTS, for your everlasting and endless support. xii | Preface CHAPTER 1 Introduction Machine learning is about extracting knowledge from data. It is a research field at the intersection of statistics, artificial intelligence, and computer science and is also known as predictive analytics or statistical learning. The application of machine learning methods has in recent years become ubiquitous in everyday life. From auto‐ matic recommendations of which movies to watch, to what food to order or which products to buy, to personalized online radio and recognizing your friends in your photos, many modern websites and devices have machine learning algorithms at their core. When you look at a complex website like Facebook, Amazon, or Netflix, it is very likely that every part of the site contains multiple machine learning models. Outside of commercial applications, machine learning has had a tremendous influ‐ ence on the way data-driven research is done today. The tools introduced in this book have been applied to diverse scientific problems such as understanding stars, finding distant planets, discovering new particles, analyzing DNA sequences, and providing personalized cancer treatments. Your application doesn’t need to be as large-scale or world-changing as these exam‐ ples in order to benefit from machine learning, though. In this chapter, we will explain why machine learning has become so popular and discuss what kinds of problems can be solved using machine learning. Then, we will show you how to build your first machine learning model, introducing important concepts along the way. Why Machine Learning? In the early days of “intelligent” applications, many systems used handcoded rules of “if ” and “else decisions to process data or adjust to user input. Think of a spam filter whose ...
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