class_docstr: Passive Aggressive Regressor.
  
  Read more in the :ref:`User Guide <passive_aggressive>`.
  
  Parameters
  ----------
  
  C : float, default=1.0
      Maximum step size (regularization). Defaults to 1.0.
  
  fit_intercept : bool, default=True
      Whether the intercept should be estimated or not. If False, the
      data is assumed to be already centered. Defaults to True.
  
  max_iter : int, default=1000
      The maximum number of passes over the training data (aka epochs).
      It only impacts the behavior in the ``fit`` method, and not the
      :meth:`partial_fit` method.
  
      .. versionadded:: 0.19
  
  tol : float or None, default=1e-3
      The stopping criterion. If it is not None, the iterations will stop
      when (loss > previous_loss - tol).
  
      .. versionadded:: 0.19
  
  early_stopping : bool, default=False
      Whether to use early stopping to terminate training when validation.
      score is not improving. If set to True, it will automatically set aside
      a fraction of training data as validation and terminate
      training when validation score is not improving by at least tol for
      n_iter_no_change consecutive epochs.
  
      .. versionadded:: 0.20
  
  validation_fraction : float, default=0.1
      The proportion of training data to set aside as validation set for
      early stopping. Must be between 0 and 1.
      Only used if early_stopping is True.
  
      .. versionadded:: 0.20
  
  n_iter_no_change : int, default=5
      Number of iterations with no improvement to wait before early stopping.
  
      .. versionadded:: 0.20
  
  shuffle : bool, default=True
      Whether or not the training data should be shuffled after each epoch.
  
  verbose : int, default=0
      The verbosity level.
  
  loss : str, default="epsilon_insensitive"
      The loss function to be used:
      epsilon_insensitive: equivalent to PA-I in the reference paper.
      squared_epsilon_insensitive: equivalent 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]
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code: def __init__(
      self,
      *,
      C=1.0,
      fit_intercept=True,
      max_iter=1000,
      tol=1e-3,
      early_stopping=False,
      validation_fraction=0.1,
      n_iter_no_change=5,
      shuffle=True,
      verbose=0,
      loss="epsilon_insensitive",
      epsilon=DEFAULT_EPSILON,
      random_state=None,
      warm_start=False,
      average=False,
  ):
      super().__init__(
          penalty=None,
          l1_ratio=0,
          epsilon=epsilon,
          eta0=1.0,
          fit_intercept=fit_intercept,
          max_iter=max_iter,
          tol=tol,
          early_stopping=early_stopping,
          validation_fraction=validation_fraction,
          n_iter_no_change=n_iter_no_change,
          shuffle=shuffle,
          verbose=verbose,
          random_state=random_state,
          warm_start=warm_start,
          average=average,
      )
      self.C = C
      self.loss = loss
  
file_path: catboost/contrib/python/scikit-learn/py3/sklearn/linear_model/_passive_aggressive.py
imports: from numbers import Real
  from ..base import _fit_context
  from ..utils._param_validation import Interval, StrOptions
  from ._stochastic_gradient import DEFAULT_EPSILON, BaseSGDClassifier, BaseSGDRegressor
label_desc: Use image node_id 1 to create a new PassiveAggressiveRegressor object from inherited base classes: BaseSGDRegressor with example: obj = PassiveAggressiveRegressor()