Machine Learning in Python: Essential Techniques for Predictive Analysis

Table of contents

1. Introduction
   1. Who This Book Is For
   2. What This Book Covers
   3. How This Book Is Structured
   4. What You Need to Use This Book
   5. Conventions
   6. Source Code
   7. Errata
2. Chapter 1 The Two Essential Algorithms for Making Predictions
   1. Why Are These Two Algorithms So Useful?
   2. What Are Penalized Regression Methods?
   3. What Are Ensemble Methods?
   4. How to Decide Which Algorithm to Use
   5. The Process Steps for Building a Predictive Model
   6. Chapter Contents and Dependencies
   7. Summary
   8. References
3. Chapter 2 Understand the Problem by Understanding the Data
   1. The Anatomy of a New Problem
   2. Classification Problems: Detecting Unexploded Mines Using Sonar
   3. Visualizing Properties of the Rocks versus Mines Data Set
   4. Real-Valued Predictions with Factor Variables: How Old Is Your Abalone?
   5. Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes
   6. Multiclass Classification Problem: What Type of Glass Is That?
   7. Summary
   8. Reference
4. Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data
   1. The Basic Problem: Understanding Function Approximation
   2. Factors Driving Algorithm Choices and Performance—Complexity and Data
   3. Measuring the Performance of Predictive Models
   4. Achieving Harmony Between Model and Data
   5. Summary
   6. References
5. Chapter 4 Penalized Linear Regression
   1. Why Penalized Linear Regression Methods Are So Useful
   2. Penalized Linear Regression: Regulating Linear Regression for Optimum Performance
   3. Solving the Penalized Linear Regression Problem
   4. Extensions to Linear Regression with Numeric Input
   5. Summary
   6. References
6. Chapter 5 Building Predictive Models Using Penalized Linear Methods
   1. Python Packages for Penalized Linear Regression
   2. Multivariable Regression: Predicting Wine Taste
   3. Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines
   4. Multiclass Classification: Classifying Crime Scene Glass Samples
   5. Summary
   6. References
7. Chapter 6 Ensemble Methods
   1. Binary Decision Trees
   2. Bootstrap Aggregation: “Bagging”
   3. Gradient Boosting
   4. Random Forest
   5. Summary
   6. References
8. Chapter 7 Building Ensemble Models with Python
   1. Solving Regression Problems with Python Ensemble Packages
   2. Coding Bagging to Predict Wine Taste
   3. Incorporating Non-Numeric Attributes in Python Ensemble Models
   4. Solving Binary Classification Problems with Python Ensemble Methods
   5. Solving Multiclass Classification Problems with Python Ensemble Methods
   6. Comparing Algorithms
   7. Summary
   8. References
9. Title page
10. Copyright
11. Dedication
12. About the Author
13. About the Technical Editor
14. Credits
15. Acknowledgments
16. EULA