sklearn.linear_model.PassiveAggressiveClassifier — scikit-learn 0.20.4 documentation (original) (raw)

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.

max_iter : int, optional

The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the fit method, and not thepartial_fit. Defaults to 5. Defaults to 1000 from 0.21, or if tol is not None.

New in version 0.19.

tol : float or None, optional

The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol). Defaults to None. Defaults to 1e-3 from 0.21.

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 : integer, optional

The verbosity level

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.

n_jobs : int or None, optional (default=None)

The number of CPUs to use to do the OVA (One Versus All, for multi-class problems) computation.None means 1 unless in a joblib.parallel_backend context.-1 means using all processors. See Glossaryfor more details.

random_state : int, RandomState instance or None, optional, default=None

The seed of the pseudo random number generator to use when shuffling the data. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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. 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.

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))

New in version 0.17: parameter class_weight to automatically weight samples.

average : bool or int, optional

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

n_iter : int, optional

The number of passes over the training data (aka epochs). Defaults to None. Deprecated, will be removed in 0.21.

Changed in version 0.19: Deprecated

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. For multiclass fits, it is the maximum over every binary fit.