Ensemble Methods for Machine Learning
暫譯: 機器學習的集成方法
Kunapuli, Gautam
- 出版商: Manning
- 出版日期: 2023-06-09
- 定價: $2,150
- 售價: 8.8 折 $1,892 (限時優惠至 2025-03-31)
- 語言: 英文
- 頁數: 352
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1617297135
- ISBN-13: 9781617297137
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相關分類:
Machine Learning
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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)
在機器學習的集成方法中，您將學習從零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，無法由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共同解決問題。通過聚合它們的輸出，這些集成模型可以靈活地提供豐富且準確的結果。 機器學習的集成方法是一本關於集成方法的指南，這些方法在數據科學競賽和實際應用中都有良好的記錄。通過實際案例研究，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。 購買印刷書籍可獲得Manning Publications提供的免費電子書，格式包括PDF、Kindle和ePub。
