|  | ## Polynomial Regression

#### Synopsis

Polynomial regression.

#### Description

This regression learning operator fits a polynomial of all attributes to the given data set. If the data set contains a label Y and three attributes X1, X2, and X3 a function of the form `Y = w0 + w1 * X1 ^ d1 + w2 * X2 ^ d2 + w3 * X3 ^ d3` will be fitted to the training data.

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- max iterations: The maximum number of iterations used for model fitting.
- replication factor: The amount of times each input variable is replicated, i.e. how many different degrees and coefficients can be applied to each variable
- max degree: The maximal degree used for the final polynomial.
- min coefficient: The minimum number used for the coefficients and the offset.
- max coefficient: The maximum number used for the coefficients and the offset.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess