Название: Ensemble Methods for Machine Learning (Final Release)
Автор: Gautam Kunapuli
Издательство: Manning Publications
Год: 2023
Страниц: 354
Язык: английский
Формат: pdf (true)
Размер: 18.7 MB

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.

about the technology
Automatically compare, contrast, and blend the output from multiple models to squeeze the best results from your data. Ensemble Machine Learning applies a “wisdom of crowds” method that dodges the inaccuracies and limitations of a single model. By basing responses on multiple perspectives, this innovative approach can deliver robust predictions even without massive datasets.

about the book
Ensemble Methods for Machine Learning teaches you practical techniques for applying multiple ML approaches simultaneously. Each chapter contains a unique case study that demonstrates a fully functional ensemble method, with examples including medical diagnosis, sentiment analysis, handwriting classification, and more. There’s no complex math or theory—you’ll learn in a visuals-first manner, with ample code for easy experimentation!

Each chapter will introduce a different ensembling technique, using a three-pronged approach. First, you’ll learn the intuition behind each ensemble method by visualizing step by step how learning actually takes place. Second, you’ll implement a basic version of each ensemble method yourself to fully understand the algorithmic nuts and bolts. Third, you’ll learn how to apply powerful ensemble libraries and tools practically.

Most chapters also come with their own case study on real-world data, drawn from applications such as handwritten digit prediction, recommendation systems, sentiment analysis, demand forecasting, and others. These case studies tackle several real-world issues where appropriate, including preprocessing and feature engineering, hyperparameter selection, efficient training techniques, and effective model evaluation.

what's inside

Bagging, boosting, and gradient boosting
Methods for classification, regression, and retrieval
Interpretability and explainability for ensemble methods
Feature engineering and ensemble diversity

about the reader
For Python programmers with Machine Learning experience.