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 thepartial_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.
- verboseint, default=0
The verbosity level.
- lossstr, 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=0.1
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 toTrue
. 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_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)
.
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]