Applied Predictive Modeling (Hardcover) - 8 Angebote vergleichen
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| Schnitt | € 72,56 | € 51,65 | € 56,58 | € 73,83 | € 90,94 |
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1
Applied Predictive Modeling (2013)
EN HC USISBN: 9781461468486 bzw. 1461468485, in Englisch, 616 Seiten, 2013. Ausgabe, Springer, gebundenes Buch, gebraucht.
Neuf à partir de: £41.24 (35 Offre)
Utilisé à partir de: £52.24 (11 Offre)
Voir la plus 46 Offres à Amazon.co.uk
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Usually dispatched within 1-2 business days, frais de port (si livré).
Von Händler/Antiquariat, -SuperBookDeals-.
HardCover. Pub Date :2013-09-12 Pages: 620 Language: English Publisher: Springer 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. Hardcover, Edition: 2013, Étiquette: Springer, Springer, Groupe de produits: Book, Publié: 2013-07-05, Date de sortie: 2013-07-05, Studio: Springer, Vente de rang: 30855.
Von Händler/Antiquariat, -SuperBookDeals-.
HardCover. Pub Date :2013-09-12 Pages: 620 Language: English Publisher: Springer 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. Hardcover, Edition: 2013, Étiquette: Springer, Springer, Groupe de produits: Book, Publié: 2013-07-05, Date de sortie: 2013-07-05, Studio: Springer, Vente de rang: 30855.
2
Applied Predictive Modeling (2013)
EN HC NWISBN: 9781461468486 bzw. 1461468485, in Englisch, 616 Seiten, 2013. Ausgabe, Springer, gebundenes Buch, neu.
Neuf à partir de: £41.24 (35 Offre)
Utilisé à partir de: £52.24 (11 Offre)
Voir la plus 46 Offres à Amazon.co.uk
Lieferung aus: Vereinigtes Königreich Großbritannien und Nordirland, Usually dispatched within 1-2 business days, frais de port (si livré).
Von Händler/Antiquariat, Greenwich_Books.
HardCover. Pub Date :2013-09-12 Pages: 620 Language: English Publisher: Springer 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. Hardcover, Edition: 2013, Étiquette: Springer, Springer, Groupe de produits: Book, Publié: 2013-07-05, Date de sortie: 2013-07-05, Studio: Springer, Vente de rang: 30855.
Von Händler/Antiquariat, Greenwich_Books.
HardCover. Pub Date :2013-09-12 Pages: 620 Language: English Publisher: Springer 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. Hardcover, Edition: 2013, Étiquette: Springer, Springer, Groupe de produits: Book, Publié: 2013-07-05, Date de sortie: 2013-07-05, Studio: Springer, Vente de rang: 30855.
3
Applied Predictive Modeling
EN HC NWISBN: 9781461468486 bzw. 1461468485, in Englisch, Springer-Verlag New York Inc. gebundenes Buch, neu.
This book provides an introduction to predictive models as well as a guide to applying them. It will serve as a useful guide for practitioners. All results can be reproduced using R.
4
Applied Predictive Modeling (2018)
EN HC NW FEISBN: 9781461468486 bzw. 1461468485, in Englisch, 600 Seiten, Springer, gebundenes Buch, neu, Erstausgabe.
Neu ab: € 59,05 (65 Angebote)
Gebraucht ab: € 54,11 (7 Angebote)
Zu den weiteren 72 Angeboten bei Amazon.de (Int.)
Lieferung aus: Deutschland, Versandfertig in 2 - 3 Werktagen, Versandkostenfrei. Tatsächliche Versandkosten können abweichen.
Von Händler/Antiquariat, BOOKS etc.
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. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D.His scholarly work centers on the application and development of statistical methodology and learning algorithms. 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. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. 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, Gebundene Ausgabe, Ausgabe: 1st ed. 2013, Corr. 2nd printing 2018, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2018-04-27, Studio: Springer, Verkaufsrang: 27776.
Von Händler/Antiquariat, BOOKS etc.
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. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D.His scholarly work centers on the application and development of statistical methodology and learning algorithms. 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. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. 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, Gebundene Ausgabe, Ausgabe: 1st ed. 2013, Corr. 2nd printing 2018, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2018-04-27, Studio: Springer, Verkaufsrang: 27776.
5
Applied Predictive Modeling (2018)
EN HC US FEISBN: 9781461468486 bzw. 1461468485, in Englisch, 600 Seiten, Springer, gebundenes Buch, gebraucht, Erstausgabe.
Neu ab: € 59,05 (65 Angebote)
Gebraucht ab: € 54,11 (7 Angebote)
Zu den weiteren 72 Angeboten bei Amazon.de (Int.)
Lieferung aus: Deutschland, Versandfertig in 6 - 10 Werktagen, Versandkostenfrei. Tatsächliche Versandkosten können abweichen.
Von Händler/Antiquariat, ErgodeBooks Ships From USA.
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. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D.His scholarly work centers on the application and development of statistical methodology and learning algorithms. 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. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. 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, Gebundene Ausgabe, Ausgabe: 1st ed. 2013, Corr. 2nd printing 2018, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2018-04-27, Studio: Springer, Verkaufsrang: 27776.
Von Händler/Antiquariat, ErgodeBooks Ships From USA.
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. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D.His scholarly work centers on the application and development of statistical methodology and learning algorithms. 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. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. 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, Gebundene Ausgabe, Ausgabe: 1st ed. 2013, Corr. 2nd printing 2018, Label: Springer, Springer, Produktgruppe: Book, Publiziert: 2018-04-27, Studio: Springer, Verkaufsrang: 27776.
6
Applied Predictive Modeling
~EN HC NWISBN: 9781461468486 bzw. 1461468485, vermutlich in Englisch, Springer-Verlag New York Inc. gebundenes Buch, neu.
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.
7
Applied Predictive Modeling
~EN NW ABISBN: 9781461468486 bzw. 1461468485, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Deutschland, Lieferzeit: 5 Tage.
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.
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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