Details zum E-Book

Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python

Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python

Dipayan Sarkar, Vijayalakshmi Natarajan

E-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.
  • 1. Get Closer to Your Data with Exploratory Data Analysis
  • 2. Getting Started with Ensemble Machine Learning
  • 3. Resampling Methods
  • 4. Statistical & Machine Learning Algorithms
  • 5. Bag the Models with Bagging
  • 6. When in Doubt, use Random Forest
  • 7. Boost up Model Performance with Boosting
  • 8. Blend it with Stacking
  • 9. Homogeneous Ensemble for Hand-Written Digits Recognition
  • 10. Heterogeneous Ensemble Classifiers for Credit Card Default Prediction
  • 11. Heterogeneous Ensemble for Sentiment Analysis using NLP
  • 12. Heterogeneous Ensemble for Multi-Label Classification for Text Categorization
  • Titel: Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
  • Autor: Dipayan Sarkar, Vijayalakshmi Natarajan
  • Originaler Titel: Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
  • ISBN: 9781789132502, 9781789132502
  • Veröffentlichungsdatum: 2019-01-31
  • Format: E-book
  • Artikelkennung: e_14ov
  • Verleger: Packt Publishing