Book Image

Ensemble Machine Learning Cookbook

By : Dipayan Sarkar, Vijayalakshmi Natarajan
Book Image

Ensemble Machine Learning Cookbook

By: Dipayan Sarkar, Vijayalakshmi Natarajan

Overview of this book

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.
Table of Contents (14 chapters)

Introduction to boosting

A boosting algorithm is an ensemble technique that helps to improve model performance and accuracy by taking a group of weak learners and combining them to form a strong learner. The idea behind boosting is that predictors should learn from mistakes that have been made by previous predictors.

Boosting algorithms have two key characteristics:

  • First, they undergo multiple iterations
  • Second, each iteration focuses on the instances that were wrongly classified by previous iterations

When an input is misclassified by a hypothesis, its weight is altered in the next iteration so that the next hypothesis can classify it correctly. More weight will be given to those that provide better performance on the training data. This process, through multiple iterations, converts weak learners into a collection of strong learners, thereby improving the model's performance...