.. _`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



**Documentation**

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

*Configuration*:


  - *alpha*

    Regularization strength; must be a positive float. Regularization
    improves the conditioning of the problem and reduces the variance of
    the estimates. Larger values specify stronger regularization.
    Alpha corresponds to ``1 / (2C)`` in other linear models such as
    :class:`~sklearn.linear_model.LogisticRegression` or
    :class:`~sklearn.svm.LinearSVC`. If an array is passed, penalties are
    assumed to be specific to the targets. Hence they must correspond in
    number. See ridge_regression for formula.


  - *kernel*

    Kernel mapping used internally. This parameter is directly passed to
    :class:`~sklearn.metrics.pairwise.pairwise_kernel`.
    If `kernel` is a string, it must be one of the metrics
    in `pairwise.PAIRWISE_KERNEL_FUNCTIONS`.
    If `kernel` is "precomputed", X is assumed to be a kernel matrix.
    Alternatively, if `kernel` is a callable function, it is called on
    each pair of instances (rows) and the resulting value recorded. The
    callable should take two rows from X as input and return the
    corresponding kernel value as a single number. This means that
    callables from :mod:`sklearn.metrics.pairwise` are not allowed, as
    they operate on matrices, not single samples. Use the string
    identifying the kernel instead.


  - *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. If
    kernel == "precomputed" this is instead the precomputed
    training matrix, of shape (n_samples, n_samples).




*Input ports*:


*Output ports*:
    **model** : model
        Model


**Definition**


*Input ports*


*Output ports*

    :model:  model

        Model





.. automodule:: node_regression
    :noindex:

.. class:: KernelRidge
    :noindex: