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) [source]
 
Passive Aggressive Regressor Read more in the User Guide.  Parameters 
 
Cfloat, default=1.0 

Maximum step size (regularization). Defaults to 1.0.  
fit_interceptbool, default=True 

Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.  
max_iterint, 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.   
tolfloat 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_stoppingbool, 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_fractionfloat, 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_changeint, default=5 

Number of iterations with no improvement to wait before early stopping.  New in version 0.20.   
shufflebool, default=True 

Whether or not the training data should be shuffled after each epoch.  
verboseinteger, default=0 

The verbosity level  
lossstring, 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.  
epsilonfloat, default=DEFAULT_EPSILON 

If the difference between the current prediction and the correct label is below this threshold, the model is not updated.  
random_stateint, 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_startbool, 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.  
averagebool 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_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).      See also  
SGDRegressor

  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]