|  | ## Additive Regression

#### Synopsis

Additive regression operator allowing all learners (not restricted to Weka learners).

#### Description

This operator uses regression learner as a base learner. The learner starts with a default model (mean or mode) as a first prediction model. In each iteration it learns a new base model and applies it to the example set. Then, the residuals of the labels are calculated and the next base model is learned. The learned meta model predicts the label by adding all base model predictions.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- example set:

#### Parameters

- iterations: The number of iterations.
- shrinkage: Reducing this learning rate prevent overfitting but increases the learning time.

#### ExampleProcess