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I regard this as aapplied counterpart tomethodology 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 t I regard this as aapplied counterpart tomethodology 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 computational example is at the end, so that the example can develop data manipulation, application of method, then model evaluation I like this as it allows forcomplex and messy data sets than when using a new, small example for each problem Also allows for better discussions when illustrating the differences between methods.2 Data manipulation data processing is given a separate chapter early on I appreciate the attention given to working with the data e.g missing value imputation There are other resources in data handling, but not in the same place as those that address the statistics methodology.3 Emphasis on model evaluation There is an early chapter devoted to model evaluation Then each major section of the book has an early chapter devoted to model evaluation of that class of problem This is in contrast to many books that are built around types of algorithms, and model evaluation is fit in Methods and algorithms are relatively easy compared to the thought process of determining what is the right thing to do It figures that this book will be strong in model evaluation when one of the authors is the lead on the caret package in R I used this as a supplement in teaching a data science course that I use a range of different resources because I need to cover working with data, model evaluation, and machine learning methods The next time I teach this course, I will use only this book because it covers all of these aspects of the field I think this book is best seen as a sequel 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 I I think this book is best seen as a sequel 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 I recently went through Data Scientist job interviews, and some of the most common questions 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 correlated The 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 ana I recently went through Data Scientist job interviews, and some of the most common questions 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 correlated The 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, andKuhn also gives useful intuitive explanations of some of thecomplicated, 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 squares 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 6 8 hours on a plane ride across country, before an interview Clearly, you could probably getout 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 An exciting book on exciting stuff. 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 Classificati 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 Models for making factor predictions.4 Other Considerations concerning model quality.Overall, Applied Predictive Modeling is a very informative course on machine learning It assumes some prior knowledge and might be difficult to access for someone without any knowledge, despite leaving out unnecessary equations Introduction to Statistical Learning by Robert Tibshirani and Trevor Hastie would be a good read before starting this book Some of the book s examples are taken from the field of medicine and pharmaceuticals which make them hard to understand for people outside of the realm of the health sciences.However, the book does a very good job at making machine learning in R muchsystematic It clearly shows the advantages of using the caret package written by the book s author and how to evaluate and tune your model s performance If you are not entirely new to data science, this book will yield a high return for you It makes your process of training a modelstraightforward and thorough Its focus on the process of constructing and validating a predictive model is excellent. |EBOOK ♺ 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 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 overyears 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 A plethora of fantastic references with great examples of how to use caret for predictive modeling in practice. 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 isfor the advanced reader who aims at appling the techniques in practice As a prerequisite you should have some basic programming knowledge and should have heared at least one statistics or better chemometrics, econometrics, etc course You do 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 isfor the advanced reader who aims at appling the techniques in practice As a prerequisite 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 mathematician.The authors provide a few theoretical equations 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 techniques that are currently used in practice You learn not only about classic regression and classification techniques, but about also decision trees, neural networks as well as rule based systems Only if you want to dig deeping into specific fields, e.g apply LSTM neural networks, you have to continue withspecialized books 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 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 using any algorithm I also found the writing style clear, well organized, and easy to read Highly Recommended