| 書目名稱 | Ensemble Methods in Data Mining |
| 副標(biāo)題 | Improving Accuracy T |
| 編輯 | Giovanni Seni,John F. Elder |
| 視頻video | http://file.papertrans.cn/312/311371/311371.mp4 |
| 叢書名稱 | Synthesis Lectures on Data Mining and Knowledge Discovery |
| 圖書封面 |  |
| 描述 | Ensemble methods have been called the most influential development in Data Mining and Machine Learning in the past decade. They combine multiple models into one usually more accurate than the best of its components. Ensembles can provide a critical boost to industrial challenges -- from investment timing to drug discovery, and fraud detection to recommendation systems -- where predictive accuracy is more vital than model interpretability. Ensembles are useful with all modeling algorithms, but this book focuses on decision trees to explain them most clearly. After describing trees and their strengths and weaknesses, the authors provide an overview of regularization -- today understood to be a key reason for the superior performance of modern ensembling algorithms. The book continues with a clear description of two recent developments: Importance Sampling (IS) and Rule Ensembles (RE). IS reveals classic ensemble methods -- bagging, random forests, and boosting -- to be special cases of a single algorithm, thereby showing how to improve their accuracy and speed. REs are linear rule models derived from decision tree ensembles. They are the most interpretable version of ensembles, which |
| 出版日期 | Book 2010 |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-01899-2 |
| isbn_softcover | 978-3-031-00771-2 |
| isbn_ebook | 978-3-031-01899-2Series ISSN 2151-0067 Series E-ISSN 2151-0075 |
| issn_series | 2151-0067 |
| copyright | Springer Nature Switzerland AG 2010 |
|Archiver|手機(jī)版|小黑屋|
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