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)

k-fold and leave-one-out cross-validation

Machine learning models often face the problem of generalization when they're applied to unseen data to make predictions. To avoid this problem, the model isn't trained using the complete dataset. Instead, the dataset is split into training and testing subsets. The model is trained on the training data and evaluated on the testing set, which it doesn't see during the training process. This is the fundamental idea behind cross-validation.

The simplest kind of cross-validation is the holdout method, which we saw in the previous recipe, Introduction to sampling. In the holdout method, when we split our data into training and testing subsets, there's a possibility that the testing set isn't that similar to the training set because of the high dimensionality of the data. This can lead to instability in the outcome...