Ensemble Machine Learning Cookbook

Sarkar, Dipayan|Natarajan, Vijayalakshmi

ISBN 10: 1789136601 / ISBN 13: 9781789136609

Published by Packt Publishing, 2019

About this Item

This book uses a recipe-based approach to showcase the power of machine learning algorithms to build ensemble models using Python libraries. Through this book, you will be able to pick up the code, understand in depth how it works, execute and implement it .

Seller Inventory # 448329995

Bibliographic Details

• Title: Ensemble Machine Learning Cookbook
• Publisher: Packt Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 1789136601
• ISBN 13: 9781789136609
• Binding: Soft cover
• Condition: New

About this title

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

About the Authors

Dipayan Sarkar holds a Masters in Economics and comes with 15+ years of experience. He has also pursued his business analytics studies from Great Lakes Institute of Management. Dipayan has won international challenges in predictive modeling and takes keen interests in the mathematics behind Machine Learning techniques. Before opting to be an independent consultant and mentor in the data science and machine learning space with various organizations, universities & educational institutions, he has served in the capacity of senior data scientist with Fortune 500 companies.

Vijayalakshmi Natarajan holds an ME in Computer Science, comes with 4 years of industry experience. She is a data science enthusiast and is a passionate trainer in the field of data science & data visualization. She takes keen interests in deep diving into Machine Learning techniques. Her specialization includes machine learning techniques in the field of image processing.