Class RidgeCVScikitsLearnNode

Ridge regression with built-in cross-validation.

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

By default, it performs Generalized Cross-Validation, which is a form of
efficient Leave-One-Out cross-validation.

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

**Parameters**

alphas : numpy array of shape [n_alphas]
    Array of alpha values to try.
    Small positive values of alpha improve the conditioning of the
    problem and reduce the variance of the estimates.
    Alpha corresponds to ``C^-1`` in other linear models such as
    LogisticRegression or LinearSVC.

fit_intercept : boolean
    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).

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

scoring : string, callable or None, optional, default: None
    A string (see model evaluation documentation) or
    a scorer callable object / function with signature
    ``scorer(estimator, X, y)``.

cv : int, cross-validation generator or an iterable, optional
    Determines the cross-validation splitting strategy.
    Possible inputs for cv are:


    - None, to use the efficient Leave-One-Out cross-validation
    - integer, to specify the number of folds.
    - An object to be used as a cross-validation generator.
    - An iterable yielding train/test splits.

    For integer/None inputs, if ``y`` is binary or multiclass,
    :class:`StratifiedKFold` used, else, :class:`KFold` is used.

    Refer :ref:`User Guide <cross_validation>` for the various
    cross-validation strategies that can be used here.

gcv_mode : {None, 'auto', 'svd', eigen'}, optional
    Flag indicating which strategy to use when performing
    Generalized Cross-Validation. Options are::


        'auto' : use svd if n_samples > n_features or when X is a sparse
                 matrix, otherwise use eigen
        'svd' : force computation via singular value decomposition of X
                (does not work for sparse matrices)
        'eigen' : force computation via eigendecomposition of X^T X

    The 'auto' mode is the default and is intended to pick the cheaper
    option of the two depending upon the shape and format of the training
    data.

store_cv_values : boolean, default=False
    Flag indicating if the cross-validation values corresponding to
    each alpha should be stored in the `cv_values_` attribute (see
    below). This flag is only compatible with `cv=None` (i.e. using
    Generalized Cross-Validation).

**Attributes**

``cv_values_`` : array, shape = [n_samples, n_alphas] or         shape = [n_samples, n_targets, n_alphas], optional
    Cross-validation values for each alpha (if `store_cv_values=True` and         `cv=None`). After `fit()` has been called, this attribute will         contain the mean squared errors (by default) or the values of the         `{loss,score}_func` function (if provided in the constructor).

``coef_`` : array, shape = [n_features] or [n_targets, n_features]
    Weight vector(s).

``intercept_`` : float | array, shape = (n_targets,)
    Independent term in decision function. Set to 0.0 if
    ``fit_intercept = False``.

``alpha_`` : float
    Estimated regularization parameter.

See also

Ridge: Ridge regression
RidgeClassifier: Ridge classifier
RidgeClassifierCV: Ridge classifier with built-in cross validation

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.ridge.RidgeCV 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 Ridge regression model

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

Parameters

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

Training data

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

Target values

sample_weight : float or array-like of shape [n_samples]

Sample weight

Returns

self : Returns self.

Overrides: Node.stop_training