Ensemble Methods for Machine Learning

Kunapuli, Gautam

Ensemble Methods for Machine Learning
  • 出版商: Manning
  • 出版日期: 2023-06-09
  • 售價: $2,150
  • 貴賓價: 9.5 折 $2,043
  • 語言: 英文
  • 頁數: 352
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1617297135
  • ISBN-13: 9781617297137
  • 相關分類: Machine Learning
  • 相關翻譯: 集成學習實戰 (簡中版)

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商品描述

In Ensemble Methods for Machine Learning you'll learn to implement the most important ensemble machine learning methods from scratch.

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.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

商品描述(中文翻譯)

在《集成方法機器學習》一書中，您將學習從頭開始實現最重要的集成機器學習方法。

許多機器學習問題過於複雜，無法僅靠單一模型或算法解決。集成機器學習訓練一組多樣化的機器學習模型，使其共同合作解決問題。通過聚合它們的輸出，這些集成模型能夠靈活地提供豐富且準確的結果。

《集成方法機器學習》是一本介紹在數據科學競賽和實際應用中具有證明紀錄的集成方法的指南。通過實際案例研究的學習，您將深入了解基礎集成學習算法，以提供準確且高效的模型。

購買印刷版書籍將包含一本免費的電子書（PDF、Kindle和ePub格式），由Manning Publications提供。

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