This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)-this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for…mehr
This book is about predictive modeling. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this as a survey of predictive models, both statistical and machine learning. We define A predictive model as a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, the reader should have a basic understanding of statistics (statistical inference, models, tests, etc.)-this is an advanced book. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS. The book is organized so that statistical models are presented first (hopefully in a logical order), followed by machine learning models, and then applications: uplift modeling and time series. One could use this as a textbook with problem solving in R (there are no "by-hand" exercises).
