Polynomial Regression
=====================
.. figure:: icons/polynomialregression.png
Educational widget that interactively shows regression line for different regressors.
Signals
-------
**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 it has them.
Description
-----------
This widget interactively shows regression line using any of the regressors from *Orange3 Regression* module.
In the widget, `polynomial expansion `__ can be set.
Polynomial expansion is a regulation of the degree of 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.
.. figure:: images/polynomial-regression-stamped.png
1. Regressor name.
2. *Input*: independent variable on axis x.
*Polynomial expansion*: degree of polynomial expansion.
*Target*: dependent variable on axis y.
3. *Save Image* saves the image to the computer in a .svg or .png
format.
*Report* includes widget parameters and visualization in the report.
Example
-------
.. figure:: images/polyregressionmain.png
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 on the figure below.
.. figure:: images/polynomial-regression-exp1.png
The line can fit better if we increase the **Polynomial expansion** parameter. Say, we set it to 3.
.. figure:: images/polynomial-regression-exp3.png
To observe different results, change **Linear Regression** to any other regression learner from Orange. Example below is done with **Regression Tree** learner.
.. figure:: images/polyregressiontree1.png
.. figure:: images/polynomial-regression-tree-exp1.png