Machine Learning by John Paul Mueller and Luca Massaron Machine Learning For Dummies® Published by: John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030-5774, www.wiley.com Copyright © 2016 by John Wiley & Sons, Inc., Hoboken, New Jersey Media and software compilation copyright © 2016 by John Wiley & Sons, Inc. All rights reserved. Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except as permitted under Sections 107 or 108 of the 1976 United States Copyright Act, without the prior written permission of the Publisher. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permissions. 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Library of Congress Control Number: 2016940023 ISBN: 978-1-119-24551-3 ISBN 978-1-119-24577-3 (ebk); ISBN ePDF 978-1-119-24575-9 (ebk) Manufactured in the United States of America 10 9 8 7 6 5 4 3 2 1 Contents at a Glance Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Part 1: Introducing How Machines Learn . . . . . . . . . . . . . . . . . . . . . 7 CHAPTER 1: Getting the Real Story about AI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 CHAPTER 2: Learning in the Age of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 CHAPTER 3: Having a Glance at the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Part 2: Preparing Your Learning Tools . . . . . . . . . . . . . . . . . . . . . . . . 45 CHAPTER 4: Installing an R Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 CHAPTER 5: Coding in R Using RStudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 CHAPTER 6: Installing a Python Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 CHAPTER 7: Coding in Python Using Anaconda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 CHAPTER 8: Exploring Other Machine Learning Tools . . . . . . . . . . . . . . . . . . . . . . . . . 137 Part 3: Getting Started with the Math Basics . . . . . . . . . . . . . . . 145 CHAPTER 9: Demystifying the Math Behind Machine Learning . . . . . . . . . . . . . . . . . 147 CHAPTER 10: Descending the Right Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 CHAPTER 11: Validating Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 CHAPTER 12: Starting with Simple Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 Part 4: Learning from Smart and Big Data . . . . . . . . . . . . . . . . . . 217 CHAPTER 13: Preprocessing Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 CHAPTER 14: Leveraging Similarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237 CHAPTER 15: Working with Linear Models the Easy Way . . . . . . . . . . . . . . . . . . . . . . . . 257 CHAPTER 16: Hitting Complexity with Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . 279 CHAPTER 17: Going a Step beyond Using Support Vector Machines . . . . . . . . . . . . . . 297 CHAPTER 18: Resorting to Ensembles of Learners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315 Part 5: Applying Learning to Real Problems . . . . . . . . . . . . . . . . 331 CHAPTER 19: Classifying Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 CHAPTER 20: Scoring Opinions and Sentiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349 CHAPTER 21: Recommending Products and Movies . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Part 6: The Part of Tens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 CHAPTER 22: Ten Machine Learning Packages to Master . . . . . . . . . . . . . . . . . . . . . . . . 385 CHAPTER 23: Ten Ways to Improve Your Machine Learning Models . . . . . . . . . . . . . . 391 INDEX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399 Table of Contents INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 About This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Foolish Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Icons Used in This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Beyond the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Where to Go from Here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 PART 1: INTRODUCING HOW MACHINES LEARN . . . . . . . . . . . 7 CHAPTER 1: Getting the Real Story about AI . . . . . . . . . . . . . . . . . . . . . . . . 9 Moving beyond the Hype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Dreaming of Electric Sheep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Understanding the history of AI and m achine learning . . . . . . . . . . 12 Exploring what machine learning can do for AI . . . . . . . . . . . . . . . . 13 Considering the goals of machine learning . . . . . . . . . . . . . . . . . . . . 13 Defining machine learning limits based on hardware . . . . . . . . . . . 14 Overcoming AI Fantasies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Discovering the fad uses of AI and machine learning . . . . . . . . . . . 16 Considering the true uses of AI and m achine learning . . . . . . . . . . 16 Being useful; being mundane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Considering the Relationship between AI and Machine Learning . . . . 19 Considering AI and Machine Learning Specifications . . . . . . . . . . . . . . 20 Defining the Divide between Art and Engineering . . . . . . . . . . . . . . . . . 20 CHAPTER 2: Learning in the Age of Big Data . . . . . . . . . . . . . . . . . . . . . . . 23 Defining Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Considering the Sources of Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Building a new data source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Using existing data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Locating test data sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 Specifying the Role of Statistics in Machine Learning . . . . . . . . . . . . . . 29 Understanding the Role of Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Defining what algorithms do . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Considering the five main techniques . . . . . . . . . . . . . . . . . . . . . . . . 30 Defining What Training Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 CHAPTER 3: Having a Glance at the Future . . . . . . . . . . . . . . . . . . . . . . . . 35 Creating Useful Technologies for the Future . . . . . . . . . . . . . . . . . . . . . 36 Considering the role of machine learning in robots . . . . . . . . . . . . .36 Using machine learning in health care . . . . . . . . . . . . . . . . . . . . . . . . 37 Creating smart systems for various needs . . . . . . . . . . . . . . . . . . . . 37 Table of Contents v Using machine learning in industrial settings . . . . . . . . . . . . . . . . . . 38 Understanding the role of updated p rocessors and other hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Discovering the New Work Opportunities with Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Working for a machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Working with machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Repairing machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Creating new machine learning tasks . . . . . . . . . . . . . . . . . . . . . . . . .42 Devising new machine learning e nvironments . . . . . . . . . . . . . . . . . 42 Avoiding the Potential Pitfalls of Future Technologies . . . . . . . . . . . . . 43 PART 2: PREPARING YOUR LEARNING TOOLS . . . . . . . . . . . . . .45 CHAPTER 4: Installing an R Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Choosing an R Distribution with Machine Learning in Mind . . . . . . . . . 48 Installing R on Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 Installing R on Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 Installing R on Mac OS X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Downloading the Datasets and Example Code . . . . . . . . . . . . . . . . . . . . 59 Understanding the datasets used in this book . . . . . . . . . . . . . . . . . 59 Defining the code repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 CHAPTER 5: Coding in R Using RStudio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 Understanding the Basic Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Working with Vectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Organizing Data Using Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 Working with Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 Creating a basic matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 Changing the vector arrangement . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Accessing individual elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Naming the rows and columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 Interacting with Multiple Dimensions Using Arrays . . . . . . . . . . . . . . . . 71 Creating a basic array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 Naming the rows and columns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Creating a Data Frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Understanding factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 Creating a basic data frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 Interacting with data frames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 Expanding a data frame . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 Performing Basic Statistical Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Making decisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Working with loops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 vi Machine Learning For Dummies Performing looped tasks without loops . . . . . . . . . . . . . . . . . . . . . . . 84 Working with functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Finding mean and median . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 Charting your data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87 CHAPTER 6: Installing a Python Distribution . . . . . . . . . . . . . . . . . . . . . . 89 Choosing a Python Distribution with Machine Learning in Mind . . . . . 90 Getting Continuum Analytics Anaconda . . . . . . . . . . . . . . . . . . . . . . 91 Getting Enthought Canopy Express . . . . . . . . . . . . . . . . . . . . . . . . . . 92 Getting pythonxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Getting WinPython . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Installing Python on Linux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Installing Python on Mac OS X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 Installing Python on Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 Downloading the Datasets and Example Code . . . . . . . . . . . . . . . . . . . . 99 Using Jupyter Notebook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 Defining the code repository . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 Understanding the datasets used in this book . . . . . . . . . . . . . . . . 106 CHAPTER 7: Coding in Python Using Anaconda . . . . . . . . . . . . . . . . . . . 109 Working with Numbers and Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 Performing variable assignments . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 Doing arithmetic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Comparing data using Boolean expressions . . . . . . . . . . . . . . . . . . 115 Creating and Using Strings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Interacting with Dates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 Creating and Using Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Creating reusable functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Calling functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 Working with global and local variables . . . . . . . . . . . . . . . . . . . . . .123 Using Conditional and Loop Statements . . . . . . . . . . . . . . . . . . . . . . . .124 Making decisions using the if statement . . . . . . . . . . . . . . . . . . . . . 124 Choosing between multiple options using nested decisions . . . . 125 Performing repetitive tasks using for . . . . . . . . . . . . . . . . . . . . . . . .126 Using the while statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Storing Data Using Sets, Lists, and Tuples . . . . . . . . . . . . . . . . . . . . . . . 128 Creating sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Performing operations on sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Creating lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .129 Creating and using tuples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 Defining Useful Iterators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 Indexing Data Using Dictionaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Storing Code in Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 Table of Contents vii CHAPTER 8: Exploring Other Machine Learning Tools . . . . . . . . . . 137 Meeting the Precursors SAS, Stata, and SPSS . . . . . . . . . . . . . . . . . . . . 138 Learning in Academia with Weka . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 Accessing Complex Algorithms Easily Using LIBSVM . . . . . . . . . . . . . .141 Running As Fast As Light with Vowpal Wabbit . . . . . . . . . . . . . . . . . . . 142 Visualizing with Knime and RapidMiner . . . . . . . . . . . . . . . . . . . . . . . . . 143 Dealing with Massive Data by Using Spark . . . . . . . . . . . . . . . . . . . . . . 144 PART 3: GETTING STARTED WITH THE MATH BASICS . . . . . 145 Demystifying the Math Behind CHAPTER 9: Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Working with Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .148 Creating a matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .150 Understanding basic operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Performing matrix multiplication . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Glancing at advanced matrix operations . . . . . . . . . . . . . . . . . . . . . 155 Using vectorization effectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 Exploring the World of Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . .158 Operating on probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 Conditioning chance by Bayes’ theorem . . . . . . . . . . . . . . . . . . . . . 160 Describing the Use of Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 CHAPTER 10: Descending the Right Curve . . . . . . . . . . . . . . . . . . . . . . . . . . 167 Interpreting Learning As Optimization . . . . . . . . . . . . . . . . . . . . . . . . . .168 Supervised learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .168 Unsupervised learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Reinforcement learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 The learning process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Exploring Cost Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Descending the Error Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174 Updating by Mini-Batch and Online . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 CHAPTER 11: Validating Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 181 Checking Out-of-Sample Errors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .182 Looking for generalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 Getting to Know the Limits of Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Keeping Model Complexity in Mind . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 Keeping Solutions Balanced . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .188 Depicting learning curves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 Training, Validating, and Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Resorting to Cross-Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 Looking for Alternatives in Validation . . . . . . . . . . . . . . . . . . . . . . . . . . .193 viii Machine Learning For Dummies