The combination of classifiers can help reduce misclassification errors substantially. Many studies have proved such ensembling methods can significantly reduce the variance of the prediction model. Several techniques have been proposed to achieve a variance reduction. For example, in many cases, bootstrap aggregating (bagging) classification trees have been shown to have higher accuracy than a single classification tree. Bagging can be applied to tree-based algorithms to enhance the accuracy of the predictions, although it can be used with methods other than tree-based methods as well.
Ensemble Machine Learning Cookbook
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Ensemble Machine Learning Cookbook
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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.
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Current Title:
Ensemble Machine Learning Cookbook
Table of Contents (14 chapters)
Preface
Free Chapter
Get Closer to Your Data
Getting Started with Ensemble Machine Learning
Resampling Methods
Statistical and Machine Learning Algorithms
Bag the Models with Bagging
When in Doubt, Use Random Forests
Boosting Model Performance with Boosting
Blend It with Stacking
Homogeneous Ensembles Using Keras
Heterogeneous Ensemble Classifiers Using H2O
Heterogeneous Ensemble for Text Classification Using NLP
Homogenous Ensemble for Multiclass Classification Using Keras
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