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Ensemble Machine Learning Cookbook

You're reading from Ensemble Machine Learning Cookbook Over 35 practical recipes to explore ensemble machine learning techniques using Python

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781789136609

Length 336 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Authors (2):

Vijayalakshmi Natarajan

Dipayan Sarkar

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Table of Contents (14) Chapters

Preface

1. Get Closer to Your Data

2. Getting Started with Ensemble Machine Learning FREE CHAPTER

3. Resampling Methods

4. Statistical and Machine Learning Algorithms

5. Bag the Models with Bagging

6. When in Doubt, Use Random Forests

7. Boosting Model Performance with Boosting

8. Blend It with Stacking

9. Homogeneous Ensembles Using Keras

10. Heterogeneous Ensemble Classifiers Using H2O

11. Heterogeneous Ensemble for Text Classification Using NLP

12. Homogenous Ensemble for Multiclass Classification Using Keras

13. Other Books You May Enjoy

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Introduction

In this book, we will cover various ensemble techniques and will learn how to ensemble multiple machine learning algorithms to enhance a model's performance. We will use pandas, NumPy, scikit-learn, and Matplotlib, all of which were built for working with Python, as we will do throughout the book. By now, you should be well aware of data manipulation and exploration.

In this chapter, we will recap how to read and manipulate data in Python, how to analyze and treat missing values, and how to explore data to gain deeper insights. We will use various Python packages, such as numpy and pandas, for data manipulation and exploration, and seaborn packages for data visualization. We will continue to use some or all of these libraries in the later chapters of this book as well. We will also use the Anaconda distribution for our Python coding. If you have not installed Anaconda, you need to download it from https://www.anaconda.com/download. At the time of writing this book, the latest version of Anaconda is 5.2, and comes with both Python 3.6 and Python 2.7. We suggest you download Anaconda for Python 3.6. We will also use the HousePrices dataset, which is available on GitHub.

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Authors (2)

Sarkar

Sarkar

Aurobindo Sarkar leads a team of data scientists and engineers at Session AI, developing cloud-based ML models for in-session marketing in e-commerce and retail. As a former CTO at multiple SaaS startups, he has architected secure, scalable, and highly available AWS cloud applications. His research interests now focus on AWS-based large-scale transformer models for NLP and HFT models for the futures and options market. Aurobindo holds a bachelor's degree in engineering from IIT Delhi, a master's in management from the Indian Institute of Science Bangalore, and a master's in computer science from New York University.

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Natarajan

Natarajan

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.

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