class_docstr: Cross-validated Least Angle Regression model.
  
  See glossary entry for :term:`cross-validation estimator`.
  
  Read more in the :ref:`User Guide <least_angle_regression>`.
  
  Parameters
  ----------
  fit_intercept : bool, default=True
      Whether to calculate the intercept for this model. If set
      to false, no intercept will be used in calculations
      (i.e. data is expected to be centered).
  
  verbose : bool or int, default=False
      Sets the verbosity amount.
  
  max_iter : int, default=500
      Maximum number of iterations to perform.
  
  precompute : bool, 'auto' or array-like , default='auto'
      Whether to use a precomputed Gram matrix to speed up
      calculations. If set to ``'auto'`` let us decide. The Gram matrix
      cannot be passed as argument since we will use only subsets of X.
  
  cv : int, cross-validation generator or an iterable, default=None
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, to use the default 5-fold cross-validation,
      - integer, to specify the number of folds.
      - :term:`CV splitter`,
      - An iterable yielding (train, test) splits as arrays of indices.
  
      For integer/None inputs, :class:`~sklearn.model_selection.KFold` is used.
  
      Refer :ref:`User Guide <cross_validation>` for the various
      cross-validation strategies that can be used here.
  
      .. versionchanged:: 0.22
          ``cv`` default value if None changed from 3-fold to 5-fold.
  
  max_n_alphas : int, default=1000
      The maximum number of points on the path used to compute the
      residuals in the cross-validation.
  
  n_jobs : int or None, default=None
      Number of CPUs to use during the cross validation.
      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
      for more details.
  
  eps : float, default=np.finfo(float).eps
      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.
  
  copy_X : bool, default=True
      If ``True``, X will be copied; else, it may be overwritten.
  
  Attributes
  ----------
  active_ : list of length n_alphas or list of such lists
      Indices of active variables at the end of the path.
      If this is a list of lists, the outer list length is `n_targets`.
  
  coef_ : array-like of shape (n_features,)
      parameter vector (w in the formulation formula)
  
  intercept_ : float
      independent term in decision function
  
  coef_path_ : array-like of shape (n_features, n_alphas)
      the varying values of the coefficients along the path
  
  alpha_ : float
      the estimated regularization parameter alpha
  
  alphas_ : array-like of shape (n_alphas,)
      the different values of alpha along the path
  
  cv_alphas_ : array-like of shape (n_cv_alphas,)
      all the values of alpha along the path for the different folds
  
  mse_path_ : array-like of 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.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  See Also
  --------
  lars_path : Compute Least Angle Regression or Lasso
      path using LARS algorithm.
  lasso_path : Compute Lasso path with coordinate descent.
  Lasso : Linear Model trained with L1 prior as
      regularizer (aka the Lasso).
  LassoCV : Lasso linear model with iterative fitting
      along a regularization path.
  LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
  LassoLarsIC : Lasso model fit with Lars using BIC
      or AIC for model selection.
  sklearn.decomposition.sparse_encode : Sparse coding.
  
  Notes
  -----
  In `fit`, once the best parameter `alpha` is found through
  cross-validation, the model is fit again using the entire training set.
  
  Examples
  --------
  >>> from sklearn.linear_model import LarsCV
  >>> from sklearn.datasets import make_regression
  >>> X, y = make_regression(n_samples=200, noise=4.0, random_state=0)
  >>> reg = LarsCV(cv=5).fit(X, y)
  >>> reg.score(X, y)
  0.9996...
  >>> reg.alpha_
  0.2961...
  >>> reg.predict(X[:1,])
  array([154.3996...])
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code: def _more_tags(self):
      return {"multioutput": False}
  
imports: import sys
  import warnings
  from math import log
  from numbers import Integral, Real
  import numpy
  from scipy import interpolate, linalg
  from scipy.linalg.lapack import get_lapack_funcs
  from ..base import MultiOutputMixin, RegressorMixin, _fit_context
  from ..exceptions import ConvergenceWarning
  from ..model_selection import check_cv
  from ..utils import Bunch, arrayfuncs, as_float_array, check_random_state
  from ..utils._metadata_requests import MetadataRouter, MethodMapping, _raise_for_params, _routing_enabled, process_routing
  from ..utils._param_validation import Hidden, Interval, StrOptions, validate_params
  from ..utils.parallel import Parallel, delayed
  from ._base import LinearModel, LinearRegression, _preprocess_data
label_desc: Use image node_id 2 for calling the LarsCV obj's underlying member method code with example usage: obj._more_tags() and returns: dict