.. _`Kernel Ridge Regression`:

.. _`org.sysess.sympathy.machinelearning.kernel_ridge`:

Kernel Ridge Regression
~~~~~~~~~~~~~~~~~~~~~~~

.. image:: kernel_ridge.svg
   :width: 48


Kernel Ridge based classifier combining ridge regression (linear least-squares L2-norm) regression with the kernel trick

:Configuration:


  - *alpha*

    Small positive values of alpha improve the conditioning of the problem
    and reduce the variance of the estimates.  Alpha corresponds to
    ``(2*C)^-1`` in other linear models such as LogisticRegression or
    LinearSVC. If an array is passed, penalties are assumed to be specific
    to the targets. Hence they must correspond in number.


  - *kernel*

    Kernel mapping used internally. A callable should accept two arguments
    and the keyword arguments passed to this object as kernel_params, and
    should return a floating point number.


  - *gamma*

    Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
    and sigmoid kernels. Interpretation of the default value is left to
    the kernel; see the documentation for sklearn.metrics.pairwise.
    Ignored by other kernels.


  - *coef0*

    Zero coefficient for polynomial and sigmoid kernels.
    Ignored by other kernels.


  - *degree*

    Degree of the polynomial kernel. Ignored by other kernels.




:Attributes:


  - *dual_coef_*

    Representation of weight vector(s) in kernel space


  - *X_fit_*

    Training data, which is also required for prediction




:Inputs:


:Outputs:
    **model** : model
        Model

*Ports*:

    **Outputs**:

        :model: model

            Model

*Configuration*:

    **alpha**
        Small positive values of alpha improve the conditioning of the problem
        and reduce the variance of the estimates.  Alpha corresponds to
        ``(2*C)^-1`` in other linear models such as LogisticRegression or
        LinearSVC. If an array is passed, penalties are assumed to be specific
        to the targets. Hence they must correspond in number.
    **kernel**
        Kernel mapping used internally. A callable should accept two arguments
        and the keyword arguments passed to this object as kernel_params, and
        should return a floating point number.
    **gamma**
        Gamma parameter for the RBF, laplacian, polynomial, exponential chi2
        and sigmoid kernels. Interpretation of the default value is left to
        the kernel; see the documentation for sklearn.metrics.pairwise.
        Ignored by other kernels.
    **coef0**
        Zero coefficient for polynomial and sigmoid kernels.
        Ignored by other kernels.
    **degree**
        Degree of the polynomial kernel. Ignored by other kernels.

.. automodule:: node_regression

.. class:: KernelRidge