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    APPLIED PREDICTIVE MODELING

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    Sinopse

    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 of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.

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    ISBN9781461468486
    Pré vendaNão
    Peso1000g
    Autor para link
    Livro disponível - pronta entregaNão
    Dimensões23.62 x 16.26 x 3.81
    IdiomaInglês
    Tipo itemLivro Importado
    Número de páginas600
    Número da edição1ª EDIÇÃO - 2013
    Código Interno1114243
    Código de barras9781461468486
    AcabamentoHARDCOVER
    AutorKUHN, MAX | JOHNSON, KJELL
    EditoraSPRINGER
    Sob encomendaSim

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