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Titlebook: Applied Predictive Modeling; Max Kuhn,Kjell Johnson Textbook 2013 Springer Science+Business Media, LLC, part of Springer Nature 2013 Model

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发表于 2025-3-21 17:58:26 | 显示全部楼层 |阅读模式
期刊全称Applied Predictive Modeling
影响因子2023Max Kuhn,Kjell Johnson
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发行地址Book specializes in data analysis with focus on practice of predictive modeling.Useful as a guide for practitioners.Reader can reproduce all results using R.Includes supplementary material:
图书封面Titlebook: Applied Predictive Modeling;  Max Kuhn,Kjell Johnson Textbook 2013 Springer Science+Business Media, LLC, part of Springer Nature 2013 Model
影响因子.Applied Predictive Modeling. covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. .This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses.  To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package..This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety
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Max Kuhn,Kjell JohnsonBook specializes in data analysis with focus on practice of predictive modeling.Useful as a guide for practitioners.Reader can reproduce all results using R.Includes supplementary material:
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https://doi.org/10.1007/978-0-387-31435-8s typically governed by a set of tuning parameters, which can allow each model to pinpoint predictive patterns and structures within the data. However, these tuning parameters can very identify predictive patterns that are not reproducible. This is known as “over-fitting.” Models that are over-fit g
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https://doi.org/10.1007/978-1-4614-6849-3Model; Non-Linear; Predictive Models; R; Regression Models; Regression Trees
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978-1-4939-7936-3Springer Science+Business Media, LLC, part of Springer Nature 2013
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