Ensemble Machine Learning Cookbook: Over 35 practical recipes to explore ensemble machine learning techniques using Python

✍ Scribed by Dipayan Sarkar, Vijayalakshmi Natarajan

• Publisher: Packt Publishing
• Year: 2019
• Tongue: English
• Leaves: 327
• Category: Library

✦ Synopsis

Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more

Key Features

• Apply popular machine learning algorithms using a recipe-based approach
• Implement boosting, bagging, and stacking ensemble methods to improve machine learning models
• Discover real-world ensemble applications and encounter complex challenges in Kaggle competitions

Book Description

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking.

The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you'll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You'll also be able to implement models such as fraud detection, text categorization, and sentiment analysis.

By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.

What you will learn

• Understand how to use machine learning algorithms for regression and classification problems
• Implement ensemble techniques such as averaging, weighted averaging, and max-voting
• Get to grips with advanced ensemble methods, such as bootstrapping, bagging, and stacking
• Use Random Forest for tasks such as classification and regression
• Implement an ensemble of homogeneous and heterogeneous machine learning algorithms
• Learn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoost

Who this book is for

This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. Working knowledge of Python programming and basic statistics is a must to help you grasp the concepts in the book.

Table of Contents

1. Get Closer to Your Data with Exploratory Data Analysis
2. Getting Started with Ensemble Machine Learning
3. Resampling Methods
4. Statistical & Machine Learning Algorithms
5. Bag the Models with Bagging
6. When in Doubt, use Random Forest
7. Boost up Model Performance with Boosting
8. Blend it with Stacking
9. Homogeneous Ensemble for Hand-Written Digits Recognition
10. Heterogeneous Ensemble Classifiers for Credit Card Default Prediction
11. Heterogeneous Ensemble for Sentiment Analysis using NLP
12. Heterogeneous Ensemble for Multi-Label Classification for Text Categorization

✦ Table of Contents

Cover
Title Page
Copyright and Credits
About Packt
Foreword
Contributors
Preface
Table of Contents
Chapter 1: Get Closer to Your Data
Introduction
Data manipulation with Python
Getting ready
How to do it...
How it works...
There's more...
See also
Analyzing, visualizing, and treating missing values
How to do it...
How it works...
There's more...
See also
Exploratory data analysis
How to do it...
How it works...
There's more...
See also
Chapter 2: Getting Started with Ensemble Machine Learning
Introduction to ensemble machine learning
Max-voting
Getting ready
How to do it...
How it works...
There's more...
Averaging
Getting ready
How to do it...
How it works...
Weighted averaging
Getting ready
How to do it...
How it works...
See also
Chapter 3: Resampling Methods
Introduction to sampling
Getting ready
How to do it...
How it works...
There's more...
See also
k-fold and leave-one-out cross-validation
Getting ready
How to do it...
How it works...
There's more...
See also
Bootstrapping
Getting ready
How to do it...
How it works...
See also
Chapter 4: Statistical and Machine Learning Algorithms
Technical requirements
Multiple linear regression
Getting ready
How to do it...
How it works...
There's more...
See also
Logistic regression
Getting ready
How to do it...
How it works...
See also
Naive Bayes
Getting ready
How to do it...
How it works...
There's more...
See also
Decision trees
Getting ready
How to do it...
How it works...
There's more...
See also
Support vector machines
Getting ready
How to do it...
How it works...
There's more...
See also
Chapter 5: Bag the Models with Bagging
Introduction
Bootstrap aggregation
Getting ready
How to do it...
How it works...
See also
Ensemble meta-estimators
Bagging classifiers
How to do it...
How it works...
There's more...
See also
Bagging regressors
Getting ready
How to do it...
How it works...
See also
Chapter 6: When in Doubt, Use Random Forests
Introduction to random forests
Implementing a random forest for predicting credit card defaults using scikit-learn
Getting ready
How to do it...
How it works...
There's more...
See also
Implementing random forest for predicting credit card defaults using H2O
Getting ready
How to do it...
How it works...
There's more...
See also
Chapter 7: Boosting Model Performance with Boosting
Introduction to boosting
Implementing AdaBoost for disease risk prediction using scikit-learn
Getting ready
How to do it...
How it works...
There's more...
See also
Implementing a gradient boosting machine for disease risk prediction using scikit-learn
Getting ready
How to do it...
How it works...
There's more...
Implementing the extreme gradient boosting method for glass identification using XGBoost with scikit-learn 
Getting ready...
How to do it...
How it works...
There's more...
See also
Chapter 8: Blend It with Stacking
Technical requirements
Understanding stacked generalization
Implementing stacked generalization by combining predictions
Getting ready...
How to do it... 
How it works...
There's more...
See also
Implementing stacked generalization for campaign outcome prediction using H2O
Getting ready...
How to do it...
How it works...
There's more...
See also
Chapter 9: Homogeneous Ensembles Using Keras
Introduction
An ensemble of homogeneous models for energy prediction
Getting ready
How to do it...
How it works...
There's more...
See also
An ensemble of homogeneous models for handwritten digit classification
Getting ready
How to do it...
How it works...
Chapter 10: Heterogeneous Ensemble Classifiers Using H2O
Introduction 
Predicting credit card defaulters using heterogeneous ensemble classifiers
Getting ready
How to do it...
How it works...
There's more...
See also
Chapter 11: Heterogeneous Ensemble for Text Classification Using NLP
Introduction
Spam filtering using an ensemble of heterogeneous algorithms
Getting ready
How to do it...
How it works...
Sentiment analysis of movie reviews using an ensemble model
Getting ready
How to do it...
How it works...
There's more...
Chapter 12: Homogenous Ensemble for Multiclass Classification Using Keras
Introduction
An ensemble of homogeneous models to classify fashion products
Getting ready
How to do it...
How it works...
See also
Other Books You May Enjoy
Index