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APPLIED PREDICTIVE MODELING

Max Kuhn; Kjell Johnson
Springer (2013)
Saatavuus: Tilaustuote
Kovakantinen kirja

83,40 €

VALUEPACK: EXPLORING CORPORATE STRATEGY: TEXT & CASES WITH KARAOKE CAPITALISM: MANAGING FOR MANKIND

Gerry Johnson; Kevan Scholes; Richard Whittington; Kjell Nordstrom; Jonas Ridderstrale
(2005)
Saatavuus: Loppuunmyyty
Pehmeäkantinen kirja

91,50 €

APPLIED PREDICTIVE MODELING

Max Kuhn; Kjell Johnson
Springer (2019)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

59,30 €

FEATURE ENGINEERING AND SELECTION - A PRACTICAL APPROACH FOR PREDICTIVE MODELS

Max Kuhn; Kjell Johnson
Taylor & Francis Ltd (2019)
Saatavuus: Tilaustuote
Kovakantinen kirja

94,60 €

FEATURE ENGINEERING AND SELECTION - A PRACTICAL APPROACH FOR PREDICTIVE MODELS

Max Kuhn; Kjell Johnson
Taylor & Francis Ltd (2021)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

63,30 €

VÄGEN TILLBAKA TILL GUD (BUNT MED FEM HÄFTEN)

Bo Johnson; Kjell Blomberg
Arcus Förlag (2022)
Saatavuus: Tilaustuote
Pehmeäkantinen kirja

24,40 €

Applied Predictive Modeling

Max Kuhn, Kjell Johnson

83,40 €

Springer
Sivumäärä: 600 sivua
Asu: Kovakantinen kirja
Painos: 2013
Julkaisuvuosi: 2013, 17.05.2013 (lisätietoa)
Kieli: Englanti

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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