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Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Prabhanjan Narayanachar Tattar

E-book
Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy.

Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you'll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models.

By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.
  • 1. Introduction to Ensemble Techniques
  • 2. Bootstrapping
  • 3. Bagging
  • 4. Random Forests
  • 5. The Bare Bones Boosting Algorithms
  • 6. Boosting Refinements
  • 7. The General Ensemble Technique
  • 8. Ensemble Diagnostics
  • 9. Ensembling Regression Models
  • 10. Ensembling Survival Models
  • 11. Ensembling Time Series Models
  • 12. What's Next?
  • Titel: Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
  • Autor: Prabhanjan Narayanachar Tattar
  • Originaler Titel: Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
  • ISBN: 9781788629171, 9781788629171
  • Veröffentlichungsdatum: 2018-07-27
  • Format: E-book
  • Artikelkennung: e_14wg
  • Verleger: Packt Publishing