Class LassoLarsCVScikitsLearnNode

Cross-validated Lasso, using the LARS algorithm

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

The optimization objective for Lasso is::


(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1

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.
    Under the positive restriction the model coefficients do not converge
    to the ordinary-least-squares solution for small values of alpha.
    Only coeffiencts up to the smallest alpha value (``alphas_[alphas_ >
    0.].min()`` when fit_path=True) reached by the stepwise Lars-Lasso
    algorithm are typically in congruence with the solution of the
    coordinate descent Lasso estimator.
    As a consequence using LassoLarsCV only makes sense for problems where
    a sparse solution is expected and/or reached.

verbose : boolean or integer, optional
    Sets the verbosity amount

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

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.

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

**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.

**Notes**


The object solves the same problem as the LassoCV object. However,
unlike the LassoCV, it find the relevant alphas values by itself.
In general, because of this property, it will be more stable.
However, it is more fragile to heavily multicollinear datasets.

It is more efficient than the LassoCV if only a small number of
features are selected compared to the total number, for instance if
there are very few samples compared to the number of features.

See also

lars_path, LassoLars, LarsCV, LassoCV

Overrides: object.__init__

Predict using the linear model

This node has been automatically generated by wrapping the sklearn.linear_model.least_angle.LassoLarsCV 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.LassoLarsCV 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