As I was writing my recent book on regression analysis (Berk, 2003), I was struck by how few alternatives to conventional regression there were. In the social sciences, for example, one either did causal modeling econometric style or largely gave up quantitative work. The life sciences did not seem quite so driven by causal modeling, but causal modeling was a popular tool. As I argued at length in my book, causal modeling as commonly undertaken is a loser.
CONTENTS
Preface
1. Statistical Learning as a Regression Problem
2. Regression Splines and Regression Smoothers
3. Classification and Regression Trees (CART)
4. Bagging
5. Random Forests
6. Boosting
7. Support Vector Machines
8. Broader Implications and a Bit of Craft Lore
References .
Index
CONTENTS
Preface
1. Statistical Learning as a Regression Problem
2. Regression Splines and Regression Smoothers
3. Classification and Regression Trees (CART)
4. Bagging
5. Random Forests
6. Boosting
7. Support Vector Machines
8. Broader Implications and a Bit of Craft Lore
References .
Index
Páginas : 378
Peso : 4mb.
Formato : PDF.
Edición : Primera
Año de Publicación :2010
ISBN : 978-1441926548
Editorial : Springer
Autor: Richard A. Berk
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