# Polynomial Regression¶

Educational widget that interactively shows regression line for different regressors.

**Inputs**

- Data: input data set. It needs at least two continuous attributes.
- Preprocessor: data preprocessors
- Learner: regression algorithm used in the widget. Default set to Linear Regression.

**Outputs**

- Learner: regression algorithm used in the widget
- Predictor: trained regressor
- Coefficients: regressor coefficients if any

## Description¶

This widget interactively shows the regression line using any of the regressors from the *Model* module. In the widget, polynomial expansion can be set. Polynomial expansion is a regulation of the degree of the polynom that is used to transform the input data and has an effect on the shape of a curve. If polynomial expansion is set to 1 it means that untransformed data are used in the regression.

- Regressor name.
*Input*: independent variable on axis x.*Polynomial expansion*: degree of polynomial expansion.*Target*: dependent variable on axis y.*Save Image*saves the image to the computer in a .svg or .png format.*Report*includes widget parameters and visualization in the report.

## Example¶

We loaded *iris* data set with the **File** widget. Then we connected **Linear Regression** learner to the **Polynomial Regression** widget. In the widget we selected *petal length* as our *Input* variable and *petal width* as our *Target* variable. We set *Polynomial expansion* to 1 which gives us a linear regression line. The result is shown in the figure below.

The line can fit better if we increase the **Polynomial expansion** parameter. Say, we set it to 3.

To observe different results, change **Linear Regression** to any other regression learner from Orange. Example below is done with the **Tree** learner.