Predictive Analytics using R

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Lulu.com, 16 gen 2015 - 552 pagine
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This book is about predictive analytics. Yet, each chapter could easily be handled by an entire volume of its own. So one might think of this a survey of predictive modeling. A predictive model is a statistical model or machine learning model used to predict future behavior based on past behavior. In order to use this book, one should have a basic understanding of mathematical statistics - it is an advanced book. Some theoretical foundations are laid out but not proven, but references are provided for additional coverage. Every chapter culminates in an example using R. R is a free software environment for statistical computing and graphics. You may download R, from a preferred CRAN mirror at http: //www.r-project.org/. 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 a textbook with problem solving in R-but there are no "by-hand" exercises.
 

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Indice

Predictive modeling
25
Modeling Techniques
41
Empirical Bayes method
53
Naïve Bayes classifier
65
Decision tree learning
87
Random forests
109
11
116
Clustering Models
145
Robust regression
277
Employing artificial neural networks
387
Criticism
393
Time Series
431
Notation Used
460
Glossary
463
References
477
Index
513

Alternative derivations
177
Logistic regression
226

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