class_docstr: Passive Aggressive Classifier
  
  Read more in the :ref:`User Guide <passive_aggressive>`.
  
  Parameters
  ----------
  
  C : float
      Maximum step size (regularization). Defaults to 1.0.
  
  fit_intercept : bool, default=False
      Whether the intercept should be estimated or not. If False, the
      data is assumed to be already centered.
  
  n_iter : int, optional
      The number of passes over the training data (aka epochs).
      Defaults to 5.
  
  shuffle : bool, default=True
      Whether or not the training data should be shuffled after each epoch.
  
  random_state : int seed, RandomState instance, or None (default)
      The seed of the pseudo random number generator to use when
      shuffling the data.
  
  verbose : integer, optional
      The verbosity level
  
  n_jobs : integer, optional
      The number of CPUs to use to do the OVA (One Versus All, for
      multi-class problems) computation. -1 means 'all CPUs'. Defaults
      to 1.
  
  loss : string, optional
      The loss function to be used:
      hinge: equivalent to PA-I in the reference paper.
      squared_hinge: equivalent to PA-II in the reference paper.
  
  warm_start : bool, optional
      When set to True, reuse the solution of the previous call to fit as
      initialization, otherwise, just erase the previous solution.
  
  class_weight : dict, {class_label: weight} or "balanced" or None, optional
      Preset for the class_weight fit parameter.
  
      Weights associated with classes. If not given, all classes
      are supposed to have weight one.
  
      The "balanced" mode uses the values of y to automatically adjust
      weights inversely proportional to class frequencies in the input data
      as ``n_samples / (n_classes * np.bincount(y))``
  
      .. versionadded:: 0.17
         parameter *class_weight* to automatically weight samples.
  
  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.
  
  See also
  --------
  
  SGDClassifier
  Perceptron
  
  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)
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code: def __init__(
      self,
      C=1.0,
      fit_intercept=True,
      n_iter=5,
      shuffle=True,
      verbose=0,
      loss="hinge",
      n_jobs=1,
      random_state=None,
      warm_start=False,
      class_weight=None,
  ):
      super(PassiveAggressiveClassifier, self).__init__(
          penalty=None,
          fit_intercept=fit_intercept,
          n_iter=n_iter,
          shuffle=shuffle,
          verbose=verbose,
          random_state=random_state,
          eta0=1.0,
          warm_start=warm_start,
          class_weight=class_weight,
          n_jobs=n_jobs,
      )
      self.C = C
      self.loss = loss
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/linear_model/passive_aggressive.py
imports: from .stochastic_gradient import BaseSGDClassifier
  from .stochastic_gradient import BaseSGDRegressor
  from .stochastic_gradient import DEFAULT_EPSILON
label_desc: Use image node_id 1 to create a new PassiveAggressiveClassifier object from inherited base classes: BaseSGDClassifier with example: obj = PassiveAggressiveClassifier(C, fit_intercept, n_iter, shuffle, verbose, loss, n_jobs, random_state, warm_start, class_weight)