Class ARDRegressionScikitsLearnNode

Bayesian ARD regression.

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

Fit the weights of a regression model, using an ARD prior. The weights of
the regression model are assumed to be in Gaussian distributions.
Also estimate the parameters lambda (precisions of the distributions of the
weights) and alpha (precision of the distribution of the noise).
The estimation is done by an iterative procedures (Evidence Maximization)

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.

threshold_lambda : float, optional
    threshold for removing (pruning) weights with high precision from
    the computation. Default is 1.e+4.

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.

``sigma_`` : array, shape = (n_features, n_features)
    estimated variance-covariance matrix of the weights

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

**Examples**

>>> from sklearn import linear_model
>>> clf = linear_model.ARDRegression()
>>> clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
... # doctest: +NORMALIZE_WHITESPACE
ARDRegression(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, threshold_lambda=10000.0, tol=0.001,
        verbose=False)
>>> clf.predict([[1, 1]])
array([ 1.])

**Notes**

See examples/linear_model/plot_ard.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.ARDRegression 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 ARDRegression model according to the given training data and parameters.

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

Iterative procedure to maximize the evidence

Parameters

X : array-like, shape = [n_samples, n_features]

Training vector, where n_samples in the number of samples and n_features is the number of features.

y : array, shape = [n_samples]

Target values (integers)

Returns

self : returns an instance of self.

Overrides: Node.stop_training