## pdf Æ Applied Predictive Modeling Á Max Kuhn

Applied Predictive ModelingFizer Global RD in Groton Connecticut He has been applying predictive models in the pharmaceutical and diagnostic industries for over 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 beginni I think this book is best seen as a seuel to An Introduction to Statistical Learning With Applications in R It has three main features Practical guidance on data preprocessing feature engineering and handling class imbalance An introduction to the caret library which offers a uniform interface to cross validation and hyperparameter tuning An overview of a larger set of models and libraries than ISLR coversDo note that the coverage of algorithms is shallower and less mathematical than ISLR If that's not what you want consider reading The Elements of Statistical Learning Data Mining Inference and Prediction Second Edition instead

### Max Kuhn Á Applied Predictive Modeling reader

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 techniues while an emphasis on problem solving with real data across a wide variety of applications will aid Applied Predictive Kindle 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 euations a mathematical background is needed for advanced topics Dr Kuhn is a Director of Non Clinical Statistics at P I recently went through Data Scientist job interviews and some of the most common uestions are related to the process or predictive modeling For example What would you do if there's a class imbalance How would you how well your model is performing What do you do if you have a lot of features and they're correlatedThe interviewers are essentially trying to assess if you understand the process of model building and that you're resourceful enough to know what to do when the analysis runs into common problems For me this book was a terrific tour of the predictive modeling process from a practitioners point of view Kuhn walks through many of these considerations such as pre processing missing data ways to evaluate your model and Kuhn also gives useful intuitive explanations of some of the complicated but best performing models in the literature While the SVM section didn't make a lot of sense I think the explanation of Neural Networks and Tree Based Methods was very insightful and really helped me understand the key ideas behind these methods and why they work I also learned many practical tips on how statisticians deal with common pitfalls in practice such as screening correlated variables and partial least suares Finally the book had a great chapter on evaluation classification models For a statistics book this was very easy to read as I actually got through it in 68 hours on a plane ride across country before an interview Clearly you could probably get out of the book by systematically working through the examples and code but I think a light read through the book was well worth it and I learned a ton

### book Applied Predictive Modeling

Applied Predictive Modeling eBook Þ Hardcover · insolpro ☆ ➹ Applied Predictive Modeling Free ➯ Author Max Kuhn – Insolpro.co.uk 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 appreciateNg 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 techniues 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 freuently 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 Great book for those who want to learn applied data science and or programming with R The book can be combined with using a R toolbox written by the authors with the identical name It contains many interesting example datasets too The book is for the advanced reader who aims at appling the techniues in practice As a prereuisite you should have some basic programming knowledge and should have heared at least one statistics or better chemometrics econometrics etc course You do not have to be a mathematicianThe authors provide a few theoretical euations in combination with great insightings from their practical experience So you will learn to study data that does not follow simple linear trends The book is pretty complete covering most stasticial techniues that are currently used in practice You learn not only about classic regression and classification techniues but about also decision trees neural networks as well as rule based systems Only if you want to dig deeping into specific fields eg apply LSTM neural networks you have to continue with specialized books