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# `sklearn.linear_model`.PassiveAggressiveRegressor

sklearn.linear_model.PassiveAggressiveRegressor(*, C=1.0, fit_intercept=True, max_iter=1000, tol=0.001, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, shuffle=True, verbose=0, loss='epsilon_insensitive', epsilon=0.1, random_state=None, warm_start=False, average=False)

  Passive Aggressive Regressor.

  Read more in the User Guide.

  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 `partial_fit` method.

      New in version 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).

      New in version 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.

      New in version 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.

      New in version 0.20.

    n_iter_no_change int, default=5

      Number of iterations with no improvement to wait before early stopping.

      New in version 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 Glossary.

    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 the Glossary.

      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.

      New in version 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 fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 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]