Applied Predictive Modeling

This book PDF is perfect for those who love Medical genre, written by Max Kuhn and published by Springer Science & Business Media which was released on 17 May 2013 with total hardcover pages 600. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Applied Predictive Modeling books below.

Applied Predictive Modeling
Applied Predictive Modeling
Author : Max Kuhn
File Size : 41,6 Mb
Publisher : Springer Science & Business Media
Language : English
Release Date : 17 May 2013
ISBN : 9781461468493
Pages : 600 pages
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Applied Predictive Modeling by Max Kuhn Book PDF Summary

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

Applied Predictive Modeling by Max Kuhn,Kjell Johnson

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.

Get Book
Applied Predictive Modeling

Applied Predictive Modeling by Max Kuhn,Kjell Johnson

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.

Get Book
Feature Engineering and Selection

Feature Engineering and Selection by Max Kuhn,Kjell Johnson

The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance.

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Applied Predictive Analytics

Applied Predictive Analytics by Dean Abbott

Learn the art and science of predictive analytics — techniques that get results Predictive analytics is what translates big data into meaningful, usable business information. Written by a leading expert in the field, this guide examines the science of the underlying algorithms as well as the principles and best practices that

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Personalized Predictive Modeling in Type 1 Diabetes

Personalized Predictive Modeling in Type 1 Diabetes by Eleni I. Georga,Dimitrios I Fotiadis,Stelios K. Tigas

Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i)

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Modeling Techniques in Predictive Analytics

Modeling Techniques in Predictive Analytics by Thomas W. Miller

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real

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Applying Predictive Analytics

Applying Predictive Analytics by Richard V. McCarthy,Mary M. McCarthy,Wendy Ceccucci,Leila Halawi

This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be utilized and

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Predictive Modeling of Drug Sensitivity

Predictive Modeling of Drug Sensitivity by Ranadip Pal

Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research challenges. It covers the major mathematical techniques used for modeling drug sensitivity, and includes the requisite biological knowledge to guide a user to apply the mathematical

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