Class RidgeClassifierCVScikitsLearnNode

Ridge classifier with built-in cross-validation.

This node has been automatically generated by wrapping the ``sklearn.linear_model.ridge.RidgeClassifierCV`` 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. Currently, only the n_features >
n_samples case is handled efficiently.

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.

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

class_weight : dict or 'balanced', optional
    Weights associated with classes in the form ``{class_label: weight}``.
    If not given, all classes are supposed to have weight one.

    The "balanced" mode uses the values of y to automatically adjust
    weights inversely proportional to class frequencies in the input data
    as ``n_samples / (n_classes * np.bincount(y))``

**Attributes**

``cv_values_`` : array, shape = [n_samples, n_alphas] or     shape = [n_samples, n_responses, 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
RidgeCV: Ridge regression with built-in cross validation

**Notes**

For multi-class classification, n_class classifiers are trained in
a one-versus-all approach. Concretely, this is implemented by taking
advantage of the multi-variate response support in Ridge.

Overrides: object.__init__

Overrides: Node.is_invertible

(inherited documentation)

Overrides: Node.is_trainable

Predict class labels for samples in X.

This node has been automatically generated by wrapping the sklearn.linear_model.ridge.RidgeClassifierCV 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]

Predicted class label per sample.

Overrides: ClassifierNode.label

Fit the ridge classifier.

This node has been automatically generated by wrapping the sklearn.linear_model.ridge.RidgeClassifierCV 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 vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape (n_samples,)

Target values.

sample_weight : float or numpy array of shape (n_samples,)

Sample weight.

Returns

self : object

Returns self.

Overrides: Node.stop_training