Ensemble Methods for Machine Learning

Gautam Kunapuli (Autor)

Buch | Softcover

350 Seiten

2023
Manning Publications (Verlag)
978-1-61729-713-7 (ISBN)

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Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning models to work together to solve a problem. By aggregating their output, these ensemble models can flexibly deliver rich and accurate results. Ensemble Methods for Machine Learning is a guide to ensemble methods with proven records in data science competitions and real world applications. Learning from hands-on case studies, you'll develop an under-the-hood understanding of foundational ensemble learning algorithms to deliver accurate, performant models.
About the Technology Ensemble machine learning lets you make robust predictions without needing the huge datasets and processing power demanded by deep learning. It sets multiple models to work on solving a problem, combining their results for better performance than a single model working alone. This "wisdom of crowds" approach distils information from several models into a set of highly accurate results.

Gautam Kunapuli has over 15 years of experience in academia and the machine learning industry. He has developed several novel algorithms for diverse application domains including social network analysis, text and natural language processing, behaviour mining, educational data mining and biomedical applications. He has also published papers exploring ensemble methods in relational domains and with imbalanced data.

Erscheinungsdatum	27.04.2023
Verlagsort	New York
Sprache	englisch
Maße	186 x 234 mm
Gewicht	640 g
Themenwelt	Mathematik / Informatik ► Informatik ► Software Entwicklung
Themenwelt	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
ISBN-10	1-61729-713-5 / 1617297135
ISBN-13	978-1-61729-713-7 / 9781617297137
Zustand	Neuware