O'Reilly logo

Ensemble Methods in Data Mining by John Elder, Giovanni Seni

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

CHAPTER 3

Model Complexity, Model Selection and Regularization

This chapter provides an overview of model complexity, model selection, and regularization. It is intended to help the reader develop an intuition for what bias and variance are; this is important because ensemble methods succeed by reducing bias, reducing variance, or finding a good tradeoff between the two. We will present a definition for regularization and see three different implementations of it. Regularization is a variance control technique which plays an essential role in modern ensembling. We will also review cross-validation which is used to estimate “meta” parameters introduced by the regularization process. We will see that finding the optimal value of these meta-parameters ...

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required