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Learn a simpler and more effective way to analyze data and predict outcomes with Python
Machine Learning in Python shows you how to successfully analyze data using only two core machine learning algorithms, and how to apply them using Python. By focusing on two algorithm families that effectively predict outcomes, this book is able to provide full descriptions of the mechanisms at work, and the examples that illustrate the machinery with specific, hackable code. The algorithms are explained in simple terms with no complex math and applied using Python, with guidance on algorithm selection, data preparation, and using the trained models in practice. You will learn a core set of Python programming techniques, various methods of building predictive models, and how to measure the performance of each model to ensure that the right one is used. The chapters on penalized linear regression and ensemble methods dive deep into each of the algorithms, and you can use the sample code in the book to develop your own data analysis solutions.

Machine learning algorithms are at the core of data analytics and visualization. In the past, these methods required a deep background in math and statistics, often in combination with the specialized R programming language. This book demonstrates how machine learning can be implemented using the more widely used and accessible Python programming language.

Predict outcomes using linear and ensemble algorithm families
Build predictive models that solve a range of simple and complex problems
Apply core machine learning algorithms using Python
Use sample code directly to build custom solutions
Machine learning doesn´t have to be complex and highly specialized. Python makes this technology more accessible to a much wider audience, using methods that are simpler, effective, and well tested. Machine Learning in Python shows you how to do this, without requiring an extensive background in math or statistics.




Table of Contents
Introduction xxiii

Chapter 1 The Two Essential Algorithms for Making Predictions 1

Why Are These Two Algorithms So Useful? 2

What Are Penalized Regression Methods? 7

What Are Ensemble Methods? 9

How to Decide Which Algorithm to Use 11

The Process Steps for Building a Predictive Model 13

Framing a Machine Learning Problem 15

Feature Extraction and Feature Engineering 17

Determining Performance of a Trained Model 18

Chapter Contents and Dependencies 18

Summary 20

Chapter 2 Understand the Problem by Understanding the Data 23

The Anatomy of a New Problem 24

Different Types of Attributes and Labels Drive Modeling Choices 26

Things to Notice about Your New Data Set 27

Classification Problems: Detecting Unexploded Mines Using Sonar 28

Physical Characteristics of the Rocks Versus Mines Data Set 29

Statistical Summaries of the Rocks versus Mines Data Set 32

Visualization of Outliers Using Quantile ]Quantile Plot 35

Statistical Characterization of Categorical Attributes 37

How to Use Python Pandas to Summarize the

Rocks Versus Mines Data Set 37

Visualizing Properties of the Rocks versus Mines Data Set 40

Visualizing with Parallel Coordinates Plots 40

Visualizing Interrelationships between Attributes and Labels 42

Visualizing Attribute and Label Correlations Using a Heat Map 49

Summarizing the Process for Understanding Rocks versus Mines Data Set 50

Real ]Valued Predictions with Factor Variables: How Old Is Your Abalone? 50

Parallel Coordinates for Regression Problems-Visualize Variable Relationships for Abalone Problem 56

How to Use Correlation Heat Map for Regression-Visualize Pair ]Wise Correlations for the Abalone Problem 60

Real ]Valued Predictions Using Real ]Valued Attributes: Calculate How Your Wine Tastes 62

Multiclass Classification Problem: What Type of Glass Is That? 68

Summary 73

Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data 75

The Basic Problem: Understanding Function Approximation 76

Working with Training Data 76

Assessing Performance of Predictive Models 78

Factors Driving Algorithm Choices and Performance-Complexity and Data 79

Contrast Between a Simple Problem and a Complex Problem 80

Contrast Between a Simple Model and a Complex Model 82

Factors Driving Predictive Algorithm Performance 86

Choosing an Algorithm: Linear or Nonlinear? 87

Measuring the Performance of Predictive Models 88

Performance Measures for Different Types of Problems 88

Simulating Performance of Deployed Models 99

Achieving Harmony Between Model and Data 101

Choosing a Model to Balance Problem Complexity, Model Complexity, and Data Set Size 102

Using Forward Stepwise Regression to Control Overfitting 103

Evaluating and Understanding Your Predictive Model 108

Control Overfitting by Penalizing Regression

Coefficients-Ridge Regression 110

Summary 119

Chapter 4 Penalized Linear Regression 121

Why Penalized Linear Regression Methods Are So Useful 122

Extremely Fast Coefficient Estimation 122

Variable Importance Information 122

Extremely Fast Evaluation When Deployed 123

Reliable Performance 123

Sparse Solutions 123

Problem May Require Linear Model 124

When to Use Ensemble Methods 124

Penalized Linear Regression: Regulating Linear Regression for Optimum Performance 124

Training Linear Models: Minimizing Errors and More 126

Adding a Coefficient Penalty to the OLS Formulation 127

Other Useful Coefficient Penalties-Manhattan and ElasticNet 128

Why Lasso Penalty Leads to Sparse Coefficient Vectors 129

ElasticNet Penalty Includes Both Lasso and Ridge 131

Solving the Penalized Linear Regression Problem 132

Understanding Least Angle Regression and Its Relationship to Forward Stepwise Regression 132

How LARS Generates Hundreds of Models of Varying Complexity 136

Choosing the Best Model from The Hundreds LARS Generates 139

Using Glmnet: Very Fast and Very General 144

Comparison of the Mechanics of Glmnet and LARS Algorithms 145

Initializing and Iterating the Glmnet Algorithm 146

Extensions to Linear Regression with Numeric Input 151

Solving Classification Problems with Penalized Regression 151

Working with Classification Problems Having More Than Two Outcomes 155

Understanding Basis Expansion: Using Linear Methods on Nonlinear Problems 156

Incorporating Non-Numeric Attributes into Linear Methods 158

Summary 163

Chapter 5 Building Predictive Models Using Penalized Linear Methods 165

Python Packages for Penalized Linear Regression 166

Multivariable Regression: Predicting Wine Taste 167

Building and Testing a Model to Predict Wine Taste 168

Training on the Whole Data Set before Deployment 172

Basis Expansion: Improving Performance by Creating New Variables from Old Ones 178

Binary Classification: Using Penalized Linear Regression to Detect Unexploded