Machine Learning with Spark and Python: Essential Techniques for Predictive Analytics

by Michael Bowles

Overview

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code.
 
Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.

Product Details

ISBN-13:	9781119561934
Publisher:	Wiley
Publication date:	11/05/2019
Edition description:	2nd ed.
Pages:	368
Product dimensions:	7.30(w) x 9.10(h) x 0.80(d)

About the Author

MICHAEL BOWLES teaches machine learning at UC Berkeley, University of New Haven and Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as semi conductor inspection, drug design and optimization and trading in the financial markets. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses are always popular and receive great feedback from participants.

Table of Contents

Introduction xxi

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 a Quantile-Quantile Plot 34

Statistical Characterization of Categorical Attributes 35

How to Use Python Pandas to Summarize the Rocks Versus Mines Data Set 36

Visualizing Properties of the Rocks Versus Mines Data Set 39

Visualizing with Parallel Coordinates Plots 39

Visualizing Interrelationships between Attributes and Labels 41

Visualizing Attribute and Label Correlations Using a Heat Map 48

Summarizing the Process for Understanding the 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 the Abalone Problem 55

How to Use a Correlation Heat Map for Regression—Visualize Pair-Wise Correlations for the Abalone Problem 59

Real-Valued Predictions Using Real-Valued Attributes: Calculate How Your Wine Tastes 61

Multiclass Classification Problem: What Type of Glass is That? 67

Using PySpark to Understand Large Data Sets 72

Summary 75

Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data 77

The Basic Problem: Understanding Function Approximation 78

Working with Training Data 79

Assessing Performance of Predictive Models 81

Factors Driving Algorithm Choices and Performance—Complexity and Data 82

Contrast between a Simple Problem and a Complex Problem 82

Contrast between a Simple Model and a Complex Model 85

Factors Driving Predictive Algorithm Performance 89

Choosing an Algorithm: Linear or Nonlinear? 90

Measuring the Performance of Predictive Models 91

Performance Measures for Different Types of Problems 91

Simulating Performance of Deployed Models 105

Achieving Harmony between Model and Data 107

Choosing a Model to Balance Problem Complexity, Model Complexity, and Data Set Size 107

Using Forward Stepwise Regression to Control Overfitting 109

Evaluating and Understanding Your Predictive Model 114

Control Overfitting by Penalizing Regression Coefficients—Ridge Regression 116

Using PySpark for Training Penalized Regression Models on Extremely Large Data Sets 124

Summary 127

Chapter 4 Penalized Linear Regression 129

Why Penalized Linear Regression Methods are So Useful 130

Extremely Fast Coefficient Estimation 130

Variable Importance Information 131

Extremely Fast Evaluation When Deployed 131

Reliable Performance 131

Sparse Solutions 132

Problem May Require Linear Model 132

When to Use Ensemble Methods 132

Penalized Linear Regression: Regulating Linear Regression for Optimum Performance 132

Training Linear Models: Minimizing Errors and More 135

Adding a Coefficient Penalty to the OLS Formulation 136

Other Useful Coefficient Penalties—Manhattan and ElasticNet 137

Why Lasso Penalty Leads to Sparse Coefficient Vectors 138

ElasticNet Penalty Includes Both Lasso and Ridge 140

Solving the Penalized Linear Regression Problem 141

Understanding Least Angle Regression and Its Relationship to Forward Stepwise Regression 141

How LARS Generates Hundreds of Models of Varying Complexity 145

Choosing the Best Model from the Hundreds LARS Generates 147

Using Glmnet: Very Fast and Very General 152

Comparison of the Mechanics of Glmnet and LARS Algorithms 153

Initializing and Iterating the Glmnet Algorithm 153

Extension of Linear Regression to Classification Problems 157

Solving Classification Problems with Penalized Regression 157

Working with Classification Problems Having More Than Two Outcomes 161

Understanding Basis Expansion: Using Linear Methods on Nonlinear Problems 161

Incorporating Non-Numeric Attributes into Linear Methods 163

Summary 166

Chapter 5 Building Predictive Models Using Penalized Linear Methods 169

Python Packages for Penalized Linear Regression 170

Multivariable Regression: Predicting Wine Taste 171

Building and Testing a Model to Predict Wine Taste 172

Training on the Whole Data Set before Deployment 175

Basis Expansion: Improving Performance by Creating New Variables from Old Ones 179

Binary Classification: Using Penalized Linear Regression to Detect Unexploded Mines 182

Build a Rocks Versus Mines Classifier for Deployment 191

Multiclass Classification: Classifying Crime Scene Glass Samples 200

Linear Regression and Classification Using PySpark 203

Using PySpark to Predict Wine Taste 204

Logistic Regression with PySpark: Rocks Versus Mines 208

Incorporating Categorical Variables in a PySpark Model: Predicting Abalone Rings 213

Multiclass Logistic Regression with Meta Parameter Optimization 217

Summary 219

Chapter 6 Ensemble Methods 221

Binary Decision Trees 222

How a Binary Decision Tree Generates Predictions 224

How to Train a Binary Decision Tree 225

Tree Training Equals Split Point Selection 227

How Split Point Selection Affects Predictions 228

Algorithm for Selecting Split Points 229

Multivariable Tree Training—Which Attribute to Split? 229

Recursive Splitting for More Tree Depth 230

Overfitting Binary Trees 231

Measuring Overfit with Binary Trees 231

Balancing Binary Tree Complexity for Best Performance 232

Modifi cations for Classification and Categorical Features 235

Bootstrap Aggregation: “Bagging” 235

How Does the Bagging Algorithm Work? 236

Bagging Performance—Bias Versus Variance 239

How Bagging Behaves on Multivariable Problem 241

Bagging Needs Tree Depth for Performance 245

Summary of Bagging 246

Gradient Boosting 246

Basic Principle of Gradient Boosting Algorithm 246

Parameter Settings for Gradient Boosting 249

How Gradient Boosting Iterates toward a Predictive Model 249

Getting the Best Performance from Gradient Boosting 250

Gradient Boosting on a Multivariable Problem 253

Summary for Gradient Boosting 256

Random Forests 256

Random Forests: Bagging Plus Random Attribute Subsets 259

Random Forests Performance Drivers 260

Random Forests Summary 261

Summary 262

Chapter 7 Building Ensemble Models with Python 265

Solving Regression Problems with Python Ensemble Packages 265

Using Gradient Boosting to Predict Wine Taste 266

Using the Class Constructor for GradientBoostingRegressor 266

Using GradientBoostingRegressor to Implement a Regression Model 268

Assessing the Performance of a Gradient Boosting Model 271

Building a Random Forest Model to Predict Wine Taste 272

Constructing a RandomForestRegressor Object 273

Modeling Wine Taste with RandomForestRegressor 275

Visualizing the Performance of a Random Forest Regression Model 279

Incorporating Non-Numeric Attributes in Python Ensemble Models 279

Coding the Sex of Abalone for Gradient Boosting Regression in Python 280

Assessing Performance and the Importance of Coded Variables with Gradient Boosting 282

Coding the Sex of Abalone for Input to Random Forest Regression in Python 284

Assessing Performance and the Importance of Coded Variables 287

Solving Binary Classification Problems with Python Ensemble Methods 288

Detecting Unexploded Mines with Python Gradient Boosting 288

Determining the Performance of a Gradient Boosting Classifier 291

Detecting Unexploded Mines with Python Random Forest 292

Constructing a Random Forest Model to Detect Unexploded Mines 294

Determining the Performance of a Random Forest Classifier 298

Solving Multiclass Classification Problems with Python Ensemble Methods 300

Dealing with Class Imbalances 301

Classifying Glass Using Gradient Boosting 301

Determining the Performance of the Gradient Boosting Model on Glass Classification 306

Classifying Glass with Random Forests 307

Determining the Performance of the Random Forest Model on Glass Classification 310

Solving Regression Problems with PySpark Ensemble Packages 311

Predicting Wine Taste with PySpark Ensemble Methods 312

Predicting Abalone Age with PySpark Ensemble Methods 317

Distinguishing Mines from Rocks with PySpark

Ensemble Methods 321

Identifying Glass Types with PySpark Ensemble Methods 325

Summary 327

Index 329