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 powers. This book will help you to implement some popular machine-learning algorithms to cover different paradigms of ensemble machine learning, such as boosting, bagging, and stacking.
This 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'll also ensure that you don't miss out on key topics such as resampling methods. As you progress, you'll get a better understanding of bagging, boosting, stacking, and learn how to work 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, compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, Natural Language Processing (NLP), and more. You'll also be able to implement models covering 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.
