Suppose during the construction of a decision tree we wish to specify a constant regional prediction function
Question:
Suppose during the construction of a decision tree we wish to specify a constant regional prediction function \(g^{w}\) on the region \(\mathscr{R}_{w}\), based on the training data in \(\mathscr{R}_{w}\), say \(\left\{\left(\boldsymbol{x}_{1}, y_{1}\right), \ldots,\left(\boldsymbol{x}_{k}, y_{k}\right)\right\}\). Show that \(g^{w}(\boldsymbol{x}):=k^{-1} \sum_{i=1}^{k} y_{i}\) minimizes the squared-error loss.
Fantastic news! We've Found the answer you've been seeking!
Step by Step Answer:
Related Book For
Data Science And Machine Learning Mathematical And Statistical Methods
ISBN: 9781118710852
1st Edition
Authors: Dirk P. Kroese, Thomas Taimre, Radislav Vaisman, Zdravko Botev
Question Posted: