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 qby-handq exercises).Jeffrey Strickland. probability 1 a Im of coming from a normal distribution with variance I2, where Im is small, and probability Im of coming from a normal distribution with variance cI2 for some c agt; 1 ei~(1 a Im)N(0, I2) + ImN(0, cI2). Typically, Im alt; 0.1.
|Title||:||Predictive Modeling and Analytics|
|Publisher||:||Lulu.com - 2014-08-06|