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 a gradient boosting machine for disease risk prediction using scikit-learn

Gradient boosting is a machine learning technique that works on the principle of boosting, where weak learners iteratively shift their focus toward error observations that were difficult to predict in previous iterations and create an ensemble of weak learners, typically decision trees.

Gradient boosting trains models in a sequential manner, and involves the following steps:

  1. Fitting a model to the data
  2. Fitting a model to the residuals
  3. Creating a new model

While the AdaBoost model identifies errors by using weights that have been assigned to the data points, gradient boosting does the same by calculating the gradients in the loss function. The loss function is a measure of how a model is able to fit the data on which it is trained and generally depends on the type of problem being solved....