valent to PA-II in the reference
    paper.

epsilon : float, default=0.1
    If the difference between the current prediction and the correct label
    is below this threshold, the model is not updated.

random_state : int, RandomState instance, default=None
    Used to shuffle the training data, when ``shuffle`` is set to
    ``True``. Pass an int for reproducible output across multiple
    function calls.
    See :term:`Glossary <random_state>`.

warm_start : bool, default=False
    When set to True, reuse the solution of the previous call to fit as
    initialization, otherwise, just erase the previous solution.
    See :term:`the Glossary <warm_start>`.

    Repeatedly calling fit or partial_fit when warm_start is True can
    result in a different solution than when calling fit a single time
    because of the way the data is shuffled.

average : bool or int, default=False
    When set to True, computes the averaged SGD weights and stores the
    result in the ``coef_`` attribute. If set to an int greater than 1,
    averaging will begin once the total number of samples seen reaches
    average. So average=10 will begin averaging after seeing 10 samples.

    .. versionadded:: 0.19
       parameter *average* to use weights averaging in SGD.

Attributes
----------
coef_ : array, shape = [1, n_features] if n_classes == 2 else [n_classes,            n_features]
    Weights assigned to the features.

intercept_ : array, shape = [1] if n_classes == 2 else [n_classes]
    Constants in decision function.

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

n_iter_ : int
    The actual number of iterations to reach the stopping criterion.

t_ : int
    Number of weight updates performed during training.
    Same as ``(n_iter_ * n_samples + 1)``.

See Also
--------
SGDRegressor : Linear model fitted by minimizing a regularized
    empirical loss with SGD.

References
----------
Online Passive-Aggressive Algorithms
<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006).

Examples
--------
>>> from sklearn.linear_model import PassiveAggressiveRegressor
>>> from sklearn.datasets import make_regression

>>> X, y = make_regression(n_features=4, random_state=0)
>>> regr = PassiveAggressiveRegressor(max_iter=100, random_state=0,
... tol=1e-3)
>>> regr.fit(X, y)
PassiveAggressiveRegressor(max_iter=100, random_state=0)
>>> print(regr.coef_)
[20.48736655 34.18818427 67.59122734 87.94731329]
>>> print(regr.intercept_)
[-0.02306214]
>>> print(regr.predict([[0, 0, 0, 0]]))
[-0.02306214]
