Ensemble Learning

Download Ensemble Learning full books in PDF, epub, and Kindle. Read online free Ensemble Learning ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Ensemble Methods for Machine Learning

Ensemble Methods for Machine Learning
Author :
Publisher : Simon and Schuster
Total Pages : 350
Release :
ISBN-10 : 9781638356707
ISBN-13 : 163835670X
Rating : 4/5 (70X Downloads)

Book Synopsis Ensemble Methods for Machine Learning by : Gautam Kunapuli

Download or read book Ensemble Methods for Machine Learning written by Gautam Kunapuli and published by Simon and Schuster. This book was released on 2023-05-30 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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! 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. About the Author Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. Table of Contents PART 1 - THE BASICS OF ENSEMBLES 1 Ensemble methods: Hype or hallelujah? PART 2 - ESSENTIAL ENSEMBLE METHODS 2 Homogeneous parallel ensembles: Bagging and random forests 3 Heterogeneous parallel ensembles: Combining strong learners 4 Sequential ensembles: Adaptive boosting 5 Sequential ensembles: Gradient boosting 6 Sequential ensembles: Newton boosting PART 3 - ENSEMBLES IN THE WILD: ADAPTING ENSEMBLE METHODS TO YOUR DATA 7 Learning with continuous and count labels 8 Learning with categorical features 9 Explaining your ensembles


Ensemble Methods for Machine Learning Related Books

Ensemble Methods for Machine Learning
Language: en
Pages: 350
Authors: Gautam Kunapuli
Categories: Computers
Type: BOOK - Published: 2023-05-30 - Publisher: Simon and Schuster

DOWNLOAD EBOOK

Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly a
Ensemble Machine Learning
Language: en
Pages: 332
Authors: Cha Zhang
Categories: Computers
Type: BOOK - Published: 2012-02-17 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed �
Ensemble Methods
Language: en
Pages: 238
Authors: Zhi-Hua Zhou
Categories: Business & Economics
Type: BOOK - Published: 2012-06-06 - Publisher: CRC Press

DOWNLOAD EBOOK

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurat
Hands-On Ensemble Learning with Python
Language: en
Pages: 284
Authors: George Kyriakides
Categories: Computers
Type: BOOK - Published: 2019-07-19 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Combine popular machine learning techniques to create ensemble models using Python Key FeaturesImplement ensemble models using algorithms such as random forests
Ensemble Methods in Data Mining
Language: en
Pages: 127
Authors: Giovanni Seni
Categories: Computers
Type: BOOK - Published: 2010 - Publisher: Morgan & Claypool Publishers

DOWNLOAD EBOOK

"Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into on