Class BayesianRidgeScikitsLearnNode

Bayesian ridge regression

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

Fit a Bayesian ridge model and optimize the regularization parameters
lambda (precision of the weights) and alpha (precision of the noise).

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

**Parameters**

n_iter : int, optional
    Maximum number of iterations.  Default is 300.

tol : float, optional
    Stop the algorithm if w has converged. Default is 1.e-3.

alpha_1 : float, optional
    Hyper-parameter : shape parameter for the Gamma distribution prior
    over the alpha parameter. Default is 1.e-6

alpha_2 : float, optional
    Hyper-parameter : inverse scale parameter (rate parameter) for the
    Gamma distribution prior over the alpha parameter.
    Default is 1.e-6.

lambda_1 : float, optional
    Hyper-parameter : shape parameter for the Gamma distribution prior
    over the lambda parameter. Default is 1.e-6.

lambda_2 : float, optional
    Hyper-parameter : inverse scale parameter (rate parameter) for the
    Gamma distribution prior over the lambda parameter.
    Default is 1.e-6

compute_score : boolean, optional
    If True, compute the objective function at each step of the model.
    Default is False

fit_intercept : boolean, optional
    whether to calculate the intercept for this model. If set
    to false, no intercept will be used in calculations
    (e.g. data is expected to be already centered).
    Default is True.

normalize : boolean, optional, default False
    If True, the regressors X will be normalized before regression.

copy_X : boolean, optional, default True
    If True, X will be copied; else, it may be overwritten.

verbose : boolean, optional, default False
    Verbose mode when fitting the model.


**Attributes**

``coef_`` : array, shape = (n_features)
    Coefficients of the regression model (mean of distribution)

``alpha_`` : float
   estimated precision of the noise.

``lambda_`` : array, shape = (n_features)
   estimated precisions of the weights.

``scores_`` : float
    if computed, value of the objective function (to be maximized)

**Examples**

>>> from sklearn import linear_model
>>> clf = linear_model.BayesianRidge()
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
... # doctest: +NORMALIZE_WHITESPACE
BayesianRidge(alpha_1=1e-06, alpha_2=1e-06, compute_score=False,
        copy_X=True, fit_intercept=True, lambda_1=1e-06, lambda_2=1e-06,
        n_iter=300, normalize=False, tol=0.001, verbose=False)
>>> clf.predict([[1, 1]])
array([ 1.])

**Notes**

See examples/linear_model/plot_bayesian_ridge.py for an example.

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.bayes.BayesianRidge 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,)

Returns predicted values.

Overrides: Node.execute

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Fit the model

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

Parameters

X : numpy array of shape [n_samples,n_features]

Training data

y : numpy array of shape [n_samples]

Target values

Returns

self : returns an instance of self.

Overrides: Node.stop_training