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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.
Chapter 8: Ensemble Diagnostics; Technical requirements; What is ensemble diagnostics?; Ensemble diversity; Numeric prediction; Class prediction; Pairwise measure; Disagreement measure; Yule's or Q-statistic; Correlation coefficient measure; Cohen's statistic; Double-fault measure; Interra...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2018.
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| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | Chapter 8: Ensemble Diagnostics; Technical requirements; What is ensemble diagnostics?; Ensemble diversity; Numeric prediction; Class prediction; Pairwise measure; Disagreement measure; Yule's or Q-statistic; Correlation coefficient measure; Cohen's statistic; Double-fault measure; Interrating agreement; Entropy measure; Kohavi-Wolpert measure; Disagreement measure for ensemble; Measurement of interrater agreement; Summary; Chapter 9: Ensembling Regression Models; Technical requirements; Pre-processing the housing data; Visualization and variable reduction; Variable clustering. This book introduces you to the concept of ensemble learning and demonstrates how different machine learning algorithms can be combined to build efficient machine learning models. Use R to implement the popular trilogy of ensemble techniques, i.e. bagging, random forest and boosting, to build faster and more accurate machine learning models. |
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| Notas: | Regression models. |
| Descripción Física: | 1 online resource (376 pages) |
| ISBN: | 9781788629171 1788629175 9781788624145 1788624149 |