Machine Learning in Python : Essential Techniques for Predictive Analysis

**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. 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 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. This book shows readers how they can successfully analyze data using only two core machine learning algorithms---and how to do so using the popular Python programming language. These algorithms deal with common scenarios faced by all data analysts and data scientists. This book focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers a multitude of use cases (what ad to place on a web page, predicting prices in securities markets, detecting credit card fraud, etc.). 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. The author will explain in simple terms, using no complex math, how these algorithms work, and will then show how to apply them in Python. He will also provide advice on how to select from among these algorithms, and will show how to prepare the data, and how to use the trained models in practice. The author begins with an overview of the two core algorithms, explaining the types of problems solved by each one. He then introduces a core set of Python programming techniques that can be used to apply these algorithms. The author shows various techniques for building predictive models that solve a range of problems, from simple to complex; he also shows how to measure the performance of each model to ensure you use the right one. The following chapters provide a deep dive into each of the two algorithms: penalized linear regression and ensemble methods. Chapters will show how to apply each algorithm in Python. Readers can directly use the sample code to build their own solutions. '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.-- Provided by publisher The Two Essential Algorithms For Making Predictions -- Understand The Problem By Understanding The Data -- Predictive Model Building: Balancing Performance, Complexity, And Big Data -- Penalized Linear Regression -- Building Predictive Models Using Penalized Linear Methods -- Ensemble Methods -- Building Ensemble Models With Python. Michael Bowles. Includes Bibliographical References And Index. Mode Of Access: World Wide Web.