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)

Sentiment analysis of movie reviews using an ensemble model

Sentiment analysis is another widely studied research area in natural language processing (NLP). It's a popular task performed on reviews to determine the sentiments of comments provided by reviewers. In this example, we'll focus on analyzing movie review data from the Internet Movie Database (IMDb) and classifying it according to whether it is positive or negative.

We have movie reviews in .txt files that are separated into two folders: negative and positive. There are 1,000 positive reviews and 1,000 negative reviews. These files can be retrieved from GitHub.

We have divided this case study into two parts:

  • The first part is to prepare the dataset. We'll read the review files that are provided in the .txt format, append them, label them as positive or negative based on which folder they have been put...