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
Maximum step size (regularization). Defaults to 1.0.
 fit_interceptbool
Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
 max_iterint, optional (default=1000)
The maximum number of passes over the training data (aka epochs). It only impacts the behavior in the
fit
method, and not thepartial_fit
method.New in version 0.19.
 tolfloat or None, optional (default=1e3)
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, optional
The verbosity level
 lossstring, optional
The loss function to be used: epsilon_insensitive: equivalent to PAI in the reference paper. squared_epsilon_insensitive: equivalent to PAII in the reference paper.
 epsilonfloat
If the difference between the current prediction and the correct label is below this threshold, the model is not updated.
 random_stateint, 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_startbool, 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.
 averagebool 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
 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
References
Online PassiveAggressive Algorithms <http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf> K. Crammer, O. Dekel, J. Keshat, S. ShalevShwartz, 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=1e3) >>> 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]