| 書目名稱 | Predictive Analytics with KNIME | | 副標題 | Analytics for Citize | | 編輯 | Frank Acito | | 視頻video | http://file.papertrans.cn/755/754574/754574.mp4 | | 概述 | Uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding.Integrates with R and several examples are used with R nodes, in a KNIME workflow.Provides gr | | 圖書封面 |  | | 描述 | .This book is about data analytics, including problem definition, data preparation, and data analysis. A variety of techniques (e.g., regression, logistic regression, cluster analysis, neural nets, decision trees, and others) are covered with conceptual background as well as demonstrations of KNIME using each tool...The book uses KNIME, which is a comprehensive, open-source software tool for analytics that does not require coding but instead uses an intuitive drag-and-drop workflow to create a network of connected nodes on an interactive canvas. KNIME workflows provide graphic representations of each step taken in analyses, making the analyses self-documenting. The graphical documentation makes it easy to reproduce analyses, as well as to communicate methods and results to others. Integration with R is also available in KNIME, and several examples using R nodes in a KNIME workflow are demonstrated for special functions and tools not explicitly included in KNIME.. | | 出版日期 | Textbook 2023 | | 關(guān)鍵詞 | analytics; business analytics; predictive analytics; machine learning; data mining; data analytics; KNIME; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-45630-5 | | isbn_softcover | 978-3-031-45632-9 | | isbn_ebook | 978-3-031-45630-5 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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