Class LassoLarsICScikitsLearnNode

Lasso model fit with Lars using BIC or AIC for model selection

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

AIC is the Akaike information criterion and BIC is the Bayes
Information criterion. Such criteria are useful to select the value
of the regularization parameter by making a trade-off between the
goodness of fit and the complexity of the model. A good model should
explain well the data while being simple.

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

**Parameters**

criterion : 'bic' | 'aic'
    The type of criterion to use.

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 LassoLarsIC 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.

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. Can be used for
    early stopping.

eps : float, optional
    The machine-precision regularization in the computation of the
    Cholesky diagonal factors. Increase this for very ill-conditioned
    systems. Unlike the ``tol`` parameter in some iterative
    optimization-based algorithms, this parameter does not control
    the tolerance of the optimization.


**Attributes**

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

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

``alpha_`` : float
    the alpha parameter chosen by the information criterion

``n_iter_`` : int
    number of iterations run by lars_path to find the grid of
    alphas.

``criterion_`` : array, shape (n_alphas,)
    The value of the information criteria ('aic', 'bic') across all
    alphas. The alpha which has the smallest information criteria
    is chosen.

**Examples**

>>> from sklearn import linear_model
>>> clf = linear_model.LassoLarsIC(criterion='bic')
>>> clf.fit([[-1, 1], [0, 0], [1, 1]], [-1.1111, 0, -1.1111])
... # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
LassoLarsIC(copy_X=True, criterion='bic', eps=..., fit_intercept=True,
      max_iter=500, normalize=True, positive=False, precompute='auto',
      verbose=False)
>>> print(clf.coef_) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[ 0.  -1.11...]

**Notes**

The estimation of the number of degrees of freedom is given by:


"On the degrees of freedom of the lasso"
Hui Zou, Trevor Hastie, and Robert Tibshirani
Ann. Statist. Volume 35, Number 5 (2007), 2173-2192.

http://en.wikipedia.org/wiki/Akaike_information_criterion
http://en.wikipedia.org/wiki/Bayesian_information_criterion

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.LassoLarsIC 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.LassoLarsIC 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.

copy_X : boolean, optional, default True

If True, X will be copied; else, it may be overwritten.

Returns

self : object

returns an instance of self.

Overrides: Node.stop_training