_targets,)
    The dual gap at the end of the optimization for the optimal alpha
    (``alpha_``).

n_iter_ : int
    Number of iterations run by the coordinate descent solver to reach
    the specified tolerance for 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 : The Lasso is a linear model that estimates sparse coefficients.
LassoLars : Lasso model fit with Least Angle Regression a.k.a. Lars.
LassoCV : Lasso linear model with iterative fitting along a regularization
    path.
LassoLarsCV : Cross-validated Lasso using the LARS algorithm.

Notes
-----
In `fit`, once the best parameter `alpha` is found through
cross-validation, the model is fit again using the entire training set.

To avoid unnecessary memory duplication the `X` argument of the `fit`
method should be directly passed as a Fortran-contiguous numpy array.

For an example, see :ref:`examples/linear_model/plot_lasso_model_selection.py
<sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py>`.

:class:`LassoCV` leads to different results than a hyperparameter
search using :class:`~sklearn.model_selection.GridSearchCV` with a
:class:`Lasso` model. In :class:`LassoCV`, a model for a given
penalty `alpha` is warm started using the coefficients of the
closest model (trained at the previous iteration) on the
regularization path. It tends to speed up the hyperparameter
search.

Examples
--------
>>> from sklearn.linear_model import LassoCV
>>> from sklearn.datasets import make_regression
>>> X, y = make_regression(noise=4, random_state=0)
>>> reg = LassoCV(cv=5, random_state=0).fit(X, y)
>>> reg.score(X, y)
0.9993...
>>> reg.predict(X[:1,])
array([-78.4951...])
rB