Applied Predictive Modeling #2020

Applied Predictive Modeling Max Kuhn Kjell Johnson Applied Predictive Modeling 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 t

  • Title: Applied Predictive Modeling
  • Author: Max Kuhn Kjell Johnson
  • ISBN: 9781461468486
  • Page: 226
  • Format: Hardcover
  • Applied Predictive Modeling Max Kuhn Kjell Johnson 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 shouldThis 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 RD 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 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 RD 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 f
    Applied Predictive Modeling Max Kuhn Kjell Johnson

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      226 Max Kuhn Kjell Johnson
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      Published :2020-04-24T17:41:44+00:00

    One thought on “Applied Predictive Modeling”

    1. Data Science is the most exciting research and professional fields these days It is creating a lot of buzz, both within the academy as well as in the business world Detractors like to point out that most of the topics and techniques used by people who call themselves Data Scientists have been around for decades if not longer However, has often been the case that a combination of topics and methodologies becomes important and concrete enough that a truly new subfield emerges Predictive Modeling i [...]

    2. I regard this as a applied counterpart to methodology oriented resources like Elements of Statistical Learning So it applies machine learning methods that are found in readily available R libraries In addition, the author is also the lead on the caret package in R, which provides a consistent interface between a large number of the common machine learning packages.1 Built around case studies that are woven through the text For each chapter, the math stats is developed first, then the computati [...]

    3. A plethora of fantastic references with great examples of how to use caret for predictive modeling in practice.

    4. Applied Predictive Modeling by Max Kuhn and Kjell Johnson is a complete examination of essential machine learning models with a clear focus on making numeric or factorial predictions On nearly 600 pages, the Authors discuss all topics from data engineering, modeling, and performance evaluation.The core of Applied Predictive Modeling consists of four distinct chapters 1 General Strategies on how to manipulate and re sample data.2 Regression Models for making numeric predictions.3 Classification M [...]

    5. One of the best books on predictive analytics using R This book sets the standard for readability, usage of real life examples to illustrate concepts and thorough documentation of all R code used to create graphs and results within the book If you re getting into analytics using R, this book is a MUST HAVE.

    6. I work with predictive models every day, and I m also the author of multiple R packages This book is the best book I own on the topic of prediction I say that even though I don t make extensive use of machine learning models, and even though there is not a single time series model in this book when most of my work is with time series The applied focus and wealth of practical experience on real problems is an invaluable set of insights for anyone building predictive models, in any field, and usin [...]

    7. This book was written by the creator of the package caret , which is a swiss knife of machine learning and data pre processing algorithms Khun covers not only a variety of stats ML algorithms, but also delves into topics related to data preprocessing, feature selection, and Type III problems The book also has a very detailed Computational Section, where the R code is clearly laid out This book has a lot of great stuff.

    8. I read this a few years ago and it was very helpful in doing machine learning in R Very simple way of explaining complex topics The primary library covered was the Caret library.

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