Ensemble Methods for Machine Learning

Gautam Kunapuli

ISBN 10: 1617297135 / ISBN 13: 9781617297137

Published by Manning Jun 2023, 2023

About this Item

Neuware -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. 350 pp. Englisch.

Seller Inventory # 9781617297137

Bibliographic Details

• Title: Ensemble Methods for Machine Learning
• Publisher: Manning Jun 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 1617297135
• ISBN 13: 9781617297137
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 231W x 185H x 20D millimeters 231 millimeters width by 185 millimeters height by 20 millimeters depth
• Weight: 646 grams

About this title

Synopsis

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.

About the Author

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.