SVM Regression
==============

.. figure:: icons/svm-regression.png

Learns a SVM regression of its input data.

Signals
-------

**Inputs**:

-  **Data**

   A data set.

-  **Preprocessor**

   Preprocessed data.

**Outputs**:

-  **Learner**

   A SVM learning algorithm with supplied parameters.

-  **Predictor**

   A trained regressor. Signal *Predictor* sends the regressor only if signal
   *Data* is present.

Description
-----------

**SVM Regression** performs linear regression in a high dimension
feature space using an ε-intensive loss. Its estimation accuracy depends
on a good setting of C, ε and kernel parameters. The widget outputs
class predictions based on a `SVM learning algorithm <https://en.wikipedia.org/wiki/Support_vector_machine#Regression>`_.

.. figure:: images/svm-regression2-stamped.png

1. Learner/predictor name
2. Train an ε-SVR or v-SVR model and set test error bounds.
3. Set kernel, a function that transforms attribute space to a new
   feature space to fit the maximum-margin hyperplane, thus allowing the
   algorithm to create non-linear regressors. The first kernel in the
   list, however, is a
   `Linear <https://en.wikipedia.org/wiki/Linear_model>`_ kernel that
   does not require this trick, but all the others
   (`Polynomial <https://en.wikipedia.org/wiki/Polynomial_kernel>`_,
   `RBF <https://en.wikipedia.org/wiki/Radial_basis_function_kernel>`_
   and
   `Sigmoid <http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applications/#sigmoid>`_)
   do. 
   Functions that specify the kernel are presented beside their
   names, and the constants involved are:

   -  **g** for the gamma constant in kernel function (the recommended
      value is 1/k, where k is the number of the attributes, but since
      there may be no training set given to the widget the default is 0
      and the user has to set this option manually),
   -  **c** for the constant c0 in the kernel function (default 0), and
   -  **d** for the degree of the kernel (default 3).

4. Set permitted deviation from the expected value.
5. Produce a report. 
6. Press *Apply* to commit changes. Alternatively, tick the box on the left side of the *Apply* button to communicate changes automatically. 

Example
-------

Below, we use two regression models to compare predictions in the
:doc:`Predictions <../evaluation/predictions>` widget. We used the *Housing* data set. 

.. figure:: images/svm-regression-example1.png