.. _`Kernel Ridge Regression`:

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

Kernel Ridge Regression
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.. image:: kernel_ridge.svg
   :width: 48


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




Documentation
:::::::::::::

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).


Definition
::::::::::


Output ports
============

    **model**  model
        Model




Configuration
=============

    **Alpha** (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.
    **Zero coefficient** (coef0)
        Zero coefficient for polynomial and sigmoid kernels.
        Ignored by other kernels.
    **Degree** (degree)
        Degree of the polynomial kernel. Ignored by other kernels.
    **Gamma** (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.
    **Kernel** (kernel)
        Kernel mapping used internally. This parameter is directly passed to
        :class:`~sklearn.metrics.pairwise.pairwise_kernels`.
        If `kernel` is a string, it must be one of the metrics
        in `pairwise.PAIRWISE_KERNEL_FUNCTIONS` or "precomputed".
        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.









Implementation
==============

.. automodule:: node_regression
    :noindex:

.. class:: KernelRidge
    :noindex: