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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.
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.
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
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