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)

Implementing AdaBoost for disease risk prediction using scikit-learn

AdaBoost is one of the earliest boosting algorithms that was used for binary classification. It was proposed by Freund and Schapire in 1996. Many other boosting-based algorithms have since been developed on top of AdaBoost.

Another variation of adaptive boosting is known as AdaBoost-abstain. AdaBoost-abstain allows each baseline classifier to abstain from voting if its dependent feature is missing.

AdaBoost focuses on combining a set of weak learners into a strong learner. The process of an AdaBoost classifier is as follows:

  1. Initially, a short decision tree classifier is fitted onto the data. The decision tree can just have a single split, which is known as a decision stump. The overall errors are evaluated. This is the first iteration.
  2. In the second iteration, whatever data is correctly classified...