書(shū)目名稱(chēng) | Machine-learning Techniques in Economics |
副標(biāo)題 | New Tools for Predic |
編輯 | Atin Basuchoudhary,James T. Bang,Tinni Sen |
視頻video | http://file.papertrans.cn/621/620805/620805.mp4 |
概述 | Offers a guide to how machine learning techniques can improve predictive power in answering economic questions.Provides R codes to help guide the researcher in applying machine learning techniques usi |
叢書(shū)名稱(chēng) | SpringerBriefs in Economics |
圖書(shū)封面 |  |
描述 | This book develops a machine-learning framework for predicting economic growth. It can also be considered as a?primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.?. |
出版日期 | Book 2017 |
關(guān)鍵詞 | Machine learning; Data mining; Economic growth; Prediction; Ranking predictive variables; Forecasting; Eco |
版次 | 1 |
doi | https://doi.org/10.1007/978-3-319-69014-8 |
isbn_softcover | 978-3-319-69013-1 |
isbn_ebook | 978-3-319-69014-8Series ISSN 2191-5504 Series E-ISSN 2191-5512 |
issn_series | 2191-5504 |
copyright | The Author(s) 2017 |