Kunapuli G. - Ensemble Methods for Machine Learning [2023, PDF, ENG] torrent

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Ensemble Methods for Machine Learning

Год издания: 2023
Автор: Kunapuli G.

Издательство: Manning
ISBN: 978-1617297137
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Нет
Количество страниц: 354

Описание: Ensemble Machine Learning combines the power of multiple Machine Learning approaches, working together to deliver models that are highly performant and highly accurate.
Inside Ensemble Methods for Machine Learning you will find:
- Methods for classification, regression, and recommendations
- Sophisticated off-the-shelf ensemble implementations
- Random forests, boosting, and gradient boosting
- Feature engineering and ensemble diversity
- Interpretability and explainability for ensemble methods
Ensemble Machine Learning trains a diverse group of Machine Learning models to work together, aggregating their output to deliver richer results than a single model. Now in Ensemble Methods for Machine Learning you’ll discover core ensemble methods that have proven records in both Data Science competitions and real-world applications. Hands-on case studies show you how each algorithm works in production. By the time you're done, you'll know the benefits, limitations, and practical methods of applying Ensemble Machine Learning to real-world data, and be ready to build more explainable ML systems.
[spoiler="Примеры страниц"]

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[spoiler="Оглавление"]
PART 1 THE BASICS OF ENSEMBLES 1
1 Ensemble methods: Hype or hallelujah? 3
PART 2 ESSENTIAL ENSEMBLE METHODS 23
2 Homogeneous parallel ensembles: Bagging and random forests 25
3 Heterogeneous parallel ensembles: Combining strong learners 51
4 Sequential ensembles: Adaptive boosting 86
5 Sequential ensembles: Gradient boosting 116
6 Sequential ensembles: Newton boosting 157
PART 3 ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 191
7 Learning with continuous and count labels 193
8 Learning with categorical features 235
9 Explaining your ensembles 271
epilogue 315
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