Class LarsCVScikitsLearnNode

Cross-validated Least Angle Regression model

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

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

**Parameters**

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).

positive : boolean (default=False)
    Restrict coefficients to be >= 0. Be aware that you might want to
    remove fit_intercept which is set True by default.

verbose : boolean or integer, optional
    Sets the verbosity amount

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.

precompute : True | False | 'auto' | array-like
    Whether to use a precomputed Gram matrix to speed up
    calculations. If set to ``'auto'`` let us decide. The Gram
    matrix can also be passed as argument.

max_iter: integer, optional
    Maximum number of iterations to perform.

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


    - None, to use the default 3-fold 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, :class:`KFold` is used.

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

max_n_alphas : integer, optional
    The maximum number of points on the path used to compute the
    residuals in the cross-validation

n_jobs : integer, optional
    Number of CPUs to use during the cross validation. If ``-1``, use
    all the CPUs

eps : float, optional
    The machine-precision regularization in the computation of the
    Cholesky diagonal factors. Increase this for very ill-conditioned
    systems.


**Attributes**

``coef_`` : array, shape (n_features,)
    parameter vector (w in the formulation formula)

``intercept_`` : float
    independent term in decision function

``coef_path_`` : array, shape (n_features, n_alphas)
    the varying values of the coefficients along the path

``alpha_`` : float
    the estimated regularization parameter alpha

``alphas_`` : array, shape (n_alphas,)
    the different values of alpha along the path

``cv_alphas_`` : array, shape (n_cv_alphas,)
    all the values of alpha along the path for the different folds

``cv_mse_path_`` : array, shape (n_folds, n_cv_alphas)
    the mean square error on left-out for each fold along the path
    (alpha values given by ``cv_alphas``)

``n_iter_`` : array-like or int
    the number of iterations run by Lars with the optimal alpha.

See also

lars_path, LassoLars, LassoLarsCV

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.least_angle.LarsCV 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 using X, y as training data.

This node has been automatically generated by wrapping the sklearn.linear_model.least_angle.LarsCV 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,)

Target values.

Returns

self : object

returns an instance of self.

Overrides: Node.stop_training