Class KernelRidgeScikitsLearnNode

Kernel ridge regression.

This node has been automatically generated by wrapping the ``sklearn.kernel_ridge.KernelRidge`` class
from the ``sklearn`` library.  The wrapped instance can be accessed
through the ``scikits_alg`` attribute.

Kernel ridge regression (KRR) combines ridge regression (linear least
squares with l2-norm regularization) with the kernel trick. It thus
learns a linear function in the space induced by the respective kernel and
the data. For non-linear kernels, this corresponds to a non-linear
function in the original space.

The form of the model learned by KRR is identical to support vector
regression (SVR). However, different loss functions are used: KRR uses
squared error loss while support vector regression uses epsilon-insensitive
loss, both combined with l2 regularization. In contrast to SVR, fitting a
KRR model can be done in closed-form and is typically faster for
medium-sized datasets. On the other  hand, the learned model is non-sparse
and thus slower than SVR, which learns a sparse model for epsilon > 0, at
prediction-time.

This estimator has built-in support for multi-variate regression
(i.e., when y is a 2d-array of shape [n_samples, n_targets]).

Read more in the :ref:`User Guide <kernel_ridge>`.

**Parameters**

alpha : {float, array-like}, shape = [n_targets]
    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 : string or callable, default="linear"
    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 : float, default=None
    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.

degree : float, default=3
    Degree of the polynomial kernel. Ignored by other kernels.

coef0 : float, default=1
    Zero coefficient for polynomial and sigmoid kernels.
    Ignored by other kernels.

kernel_params : mapping of string to any, optional
    Additional parameters (keyword arguments) for kernel function passed
    as callable object.

**Attributes**

``dual_coef_`` : array, shape = [n_features] or [n_targets, n_features]
    Weight vector(s) in kernel space

``X_fit_`` : {array-like, sparse matrix}, shape = [n_samples, n_features]
    Training data, which is also required for prediction

**References**

* Kevin P. Murphy
  "Machine Learning: A Probabilistic Perspective", The MIT Press
  chapter 14.4.3, pp. 492-493

See also

Ridge
    Linear ridge regression.
SVR
    Support Vector Regression implemented using libsvm.

**Examples**

>>> from sklearn.kernel_ridge import KernelRidge
>>> import numpy as np
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> clf = KernelRidge(alpha=1.0)
>>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE
KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear',
            kernel_params=None)

Overrides: object.__init__

Predict using the the kernel ridge model

This node has been automatically generated by wrapping the sklearn.kernel_ridge.KernelRidge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Samples.

Returns

C : array, shape = [n_samples] or [n_samples, n_targets]

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit Kernel Ridge regression model

This node has been automatically generated by wrapping the sklearn.kernel_ridge.KernelRidge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute.

Parameters

X : {array-like, sparse matrix}, shape = [n_samples, n_features]

Training data

y : array-like, shape = [n_samples] or [n_samples, n_targets]

Target values

sample_weight : float or numpy array of shape [n_samples]

Individual weights for each sample, ignored if None is passed.

Returns

self : returns an instance of self.

Overrides: Node.stop_training