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Machine Learning in Python-Essential Techniques for Predictive Analysis.pdf

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    Machine Learning in Python®
    Contents
    Introduction
    Chapter 1 The Two Essential Algorithms for Making Predictions
    Why Are These Two Algorithms So Useful?
    What Are Penalized Regression Methods?
    What Are Ensemble Methods?
    How to Decide Which Algorithm to Use
    The Process Steps for Building a Predictive Model
    Framing a Machine Learning Problem
    Feature Extraction and Feature Engineering
    Determining Performance of a Trained Model
    Chapter Contents and Dependencies
    Summary
    Chapter 2 Understand the Problem by Understanding the Data
    The Anatomy of a New Problem
    Different Types of Attributes and Labels Drive Modeling Choices
    Things to Notice about Your New Data Set
    Classification Problems: Detecting Unexploded Mines Using Sonar
    Physical Characteristics of the Rocks Versus Mines Data Set
    Statistical Summaries of the Rocks versus Mines Data Set
    Visualization of Outliers Using Quantile-Quantile Plot
    Statistical Characterization of Categorical Attributes
    How to Use Python Pandas to Summarize the Rocks Versus Mines Data Set
    Visualizing Properties of the Rocks versus Mines Data Set
    Visualizing with Parallel Coordinates Plots
    Visualizing Interrelationships between Attributes and Labels
    Visualizing Attribute and Label Correlations Using a Heat Map
    Summarizing the Process for Understanding Rocks versus Mines Data Set
    Real-Valued Predictions with Factor Variables: How Old Is Your Abalone?
    Parallel Coordinates for Regression Problems—Visualize Variable Relationships for Abalone Problem
    How to Use Correlation Heat Map for Regression—Visualize Pair-Wise Correlations for the Abalone Problem
    Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes
    Multiclass Classification Problem: What Type of Glass Is That?
    Summary
    Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data
    The Basic Problem: Understanding Function Approximation
    Working with Training Data
    Assessing Performance of Predictive Models
    Factors Driving Algorithm Choices and Performance—Complexity and Data
    Contrast Between a Simple Problem and a Complex Problem
    Contrast Between a Simple Model and a Complex Model
    Factors Driving Predictive Algorithm Performance
    Choosing an Algorithm: Linear or Nonlinear?
    Measuring the Performance of Predictive Models
    Performance Measures for Different Types of Problems
    Simulating Performance of Deployed Models
    Achieving Harmony Between Model and Data
    Choosing a Model to Balance Problem Complexity, Model Complexity, and Data Set Size
    Using Forward Stepwise Regression to Control Overfitting
    Evaluating and Understanding Your Predictive Model
    Control Overfitting by Penalizing Regression Coefficients—Ridge Regression
    Summary
    Chapter 4 Penalized Linear Regression
    Why Penalized Linear Regression Methods Are So Useful
    Extremely Fast Coefficient Estimation
    Variable Importance Information
    Extremely Fast Evaluation When Deployed
    Reliable Performance
    Sparse Solutions
    Problem May Require Linear Model
    When to Use Ensemble Methods
    Penalized Linear Regression: Regulating Linear Regression for Optimum Performance
    Training Linear Models: Minimizing Errors and More
    Adding a Coefficient Penalty to the OLS Formulation
    Other Useful Coefficient Penalties—Manhattan and ElasticNet
    Why Lasso Penalty Leads to Sparse Coefficient Vectors
    ElasticNet Penalty Includes Both Lasso and Ridge
    Solving the Penalized Linear Regression Problem
    Understanding Least Angle Regression and Its Relationship to Forward Stepwise Regression
    How LARS Generates Hundreds of Models of Varying Complexity
    Choosing the Best Model from The Hundreds LARS Generates
    Using Glmnet: Very Fast and Very General
    Comparison of the Mechanics of Glmnet and LARS Algorithms
    Initializing and Iterating the Glmnet Algorithm
    Extensions to Linear Regression with Numeric Input
    Solving Classification Problems with Penalized Regression
    Working with Classification Problems Having More Than Two Outcomes
    Understanding Basis Expansion: Using Linear Methods on Nonlinear Problems
    Incorporating Non-Numeric Attributes into Linear Methods
    Summary
    Chapter 5 Building Predictive Models Using Penalized Linear Methods
    Python Packages for Penalized Linear Regression
    Multivariable Regression: Predicting Wine Taste
    Building and Testing a Model to Predict Wine Taste
    Training on the Whole Data Set before Deployment
    Basis Expansion: Improving Performance by Creating New Variables from Old Ones
    Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines
    Build a Rocks versus Mines Classifier for Deployment
    Multiclass Classification: Classifying Crime Scene Glass Samples
    Summary
    Chapter 6 Ensemble Methods
    Binary Decision Trees
    How a Binary Decision Tree Generates Predictions
    How to Train a Binary Decision Tree
    Tree Training Equals Split Point Selection
    How Split Point Selection Affects Predictions
    Algorithm for Selecting Split Points
    Multivariable Tree Training—Which Attribute to Split?
    Recursive Splitting for More Tree Depth
    Overfitting Binary Trees
    Measuring Overfit with Binary Trees
    Balancing Binary Tree Complexity for Best Performance
    Modifications for Classification and Categorical Features
    Bootstrap Aggregation: “Bagging”
    How Does the Bagging Algorithm Work?
    Bagging Performance—Bias versus Variance
    How Bagging Behaves on Multivariable Problem
    Bagging Needs Tree Depth for Performance
    Summary of Bagging
    Gradient Boosting
    Basic Principle of Gradient Boosting Algorithm
    Parameter Settings for Gradient Boosting
    How Gradient Boosting Iterates Toward a Predictive Model
    Getting the Best Performance from Gradient Boosting
    Gradient Boosting on a Multivariable Problem
    Summary for Gradient Boosting
    Random Forest
    Random Forests: Bagging Plus Random Attribute Subsets
    Random Forests Performance Drivers
    Random Forests Summary
    Summary
    Chapter 7 Building Ensemble Models with Python
    Solving Regression Problems with Python Ensemble Packages
    Building a Random Forest Model to Predict Wine Taste
    Constructing a RandomForestRegressor Object
    Modeling Wine Taste with RandomForestRegressor
    Visualizing the Performance of a Random Forests Regression Model
    Using Gradient Boosting to Predict Wine Taste
    Using the Class Constructor for GradientBoostingRegressor
    Using GradientBoostingRegressor to Implement a Regression Model
    Assessing the Performance of a Gradient Boosting Model
    Coding Bagging to Predict Wine Taste
    Incorporating Non-Numeric Attributes in Python Ensemble Models
    Coding the Sex of Abalone for Input to Random Forest Regression in Python
    Assessing Performance and the Importance of Coded Variables
    Coding the Sex of Abalone for Gradient Boosting Regression in Python
    Assessing Performance and the Importance of Coded Variables with Gradient Boosting
    Solving Binary Classification Problems with Python Ensemble Methods
    Detecting Unexploded Mines with Python Random Forest
    Constructing a Random Forests Model to Detect Unexploded Mines
    Determining the Performance of a Random Forests Classifier
    Detecting Unexploded Mines with Python Gradient Boosting
    Determining the Performance of a Gradient Boosting Classifier
    Solving Multiclass Classification Problems with Python Ensemble Methods
    Classifying Glass with Random Forests
    Dealing with Class Imbalances
    Classifying Glass Using Gradient Boosting
    Assessing the Advantage of Using Random Forest Base Learners with Gradient Boosting
    Comparing Algorithms
    Summary
    Index
    EULA
    Machine Learning in Python®
    Machine Learning in Python® Essential Techniques for Predictive Analysis Michael Bowles
    Machine Learning in Python® : Essential Techniques for Predictive Analysis Published by John Wiley & Sons, Inc. 10475 Crosspoint Boulevard Indianapolis, IN 46256 www.wiley.com Copyright © 2015 by John Wiley & Sons, Inc., Indianapolis, Indiana Published simultaneously in Canada ISBN: 978-1-118-96174-2 ISBN: 978-1-118-96176-6 (ebk) ISBN: 978-1-118-96175-9 (ebk) Manufactured in the United States of America 10 9 8 7 6 5 4 3 2 1 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 either the prior written permission of the Publisher, or autho- rization through payment of the appropriate per-copy fee to the Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 646-8600. 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. Limit of Liability/Disclaimer of Warranty: The publisher and the author make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation warranties of fitness for a particular purpose. No warranty may be created or extended by sales or promotional materials. The advice and strategies contained herein may not be suitable for every situation. This work is sold with the understanding that the publisher is not engaged in rendering legal, accounting, or other professional services. If professional assistance is required, the services of a competent professional person should be sought. Neither the publisher nor the author shall be liable for damages arising herefrom. The fact that an organization or Web site is referred to in this work as a citation and/or a potential source of further information does not mean that the author or the publisher endorses the information the organization or website may provide or recommendations it may make. Further, readers should be aware that Internet websites listed in this work may have changed or disap- peared between when this work was written and when it is read. For general information on our other products and services please contact our Customer Care Department within the United States at (877) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http:// booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com. Library of Congress Control Number: 2015930541 Trademarks: Wiley and the Wiley logo are trademarks or registered trademarks of John Wiley & Sons, Inc. and/or its affiliates, in the United States and other countries, and may not be used without written permission. Python is a registered trademark of Python Software Foundation. All other trademarks are the property of their respective owners. John Wiley & Sons, Inc. is not associated with any product or vendor mentioned in this book.
    To my children, Scott, Seth, and Cayley. Their blossoming lives and selves bring me more joy than anything else in this world. To my close friends David and Ron for their selfless generosity and steadfast friendship. To my friends and colleagues at Hacker Dojo in Mountain View, California, for their technical challenges and repartee. To my climbing partners. One of them, Katherine, says climbing partners make the best friends because “they see you paralyzed with fear, offer encouragement to overcome it, and celebrate when you do.”
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