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Ensemble Machine Learning Cookbook
This is the code repository for Ensemble Machine Learning Cookbook, published by Packt.
Over 35 practical recipes to explore ensemble machine learning techniques using Python
What is this book about?
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
Instructions and Navigations
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
import os
import pandas as pd
# Set working directory as per your need
os.chdir(".../.../Chapter 1")
os.getcwd()
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-12).
Software and Hardware List
| Chapter | Software required | OS required |
|---|---|---|
| 1-12 | Python 3.6 | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Related products
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Building Machine Learning Systems with Python - Third Edition [Packt] [Amazon]
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Machine Learning Algorithms - Second Edition [Packt] [Amazon]
Get to Know the Authors
Dipayan Sarkar holds a Masters in economics and comes with 15+ years of experience. He has also pursued business analytics studies from Great Lakes Institute of Management. Dipayan has won international challenges in predictive modeling and takes a keen interest in the mathematics behind machine learning techniques. Before opting to become an independent consultant and a mentor in the data science and machine learning space with various organizations, universities, and educational institutions, he served in the capacity of senior data scientist with Fortune 500 companies.
Vijayalakshmi Natarajan holds an ME in computer science and has 4 years of industry experience. She is a data science enthusiast and a passionate trainer in the fields of data science and data visualization. She takes keen interest in deep-diving into machine learning techniques. Her specializations include machine learning techniques in the field of image processing.
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