sklearn.linear_model.PassiveAggressiveRegressor
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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
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Cfloat, default=1.0
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Maximum step size (regularization). Defaults to 1.0.
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fit_interceptbool, default=True
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Whether the intercept should be estimated or not. If False, the data is assumed to be already centered. Defaults to True.
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max_iterint, default=1000
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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.
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tolfloat or None, default=1e-3
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The stopping criterion. If it is not None, the iterations will stop when (loss > previous_loss - tol).
New in version 0.19.
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early_stoppingbool, default=False
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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.
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validation_fractionfloat, default=0.1
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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.
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n_iter_no_changeint, default=5
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Number of iterations with no improvement to wait before early stopping.
New in version 0.20.
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shufflebool, default=True
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Whether or not the training data should be shuffled after each epoch.
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verboseinteger, default=0
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The verbosity level
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lossstring, default=”epsilon_insensitive”
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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.
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epsilonfloat, default=DEFAULT_EPSILON
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If the difference between the current prediction and the correct label is below this threshold, the model is not updated.
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random_stateint, RandomState instance, default=None
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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
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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.
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averagebool or int, default=False
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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
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- Attributes
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coef_array, shape = [1, n_features] if n_classes == 2 else [n_classes, n_features]
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Weights assigned to the features.
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intercept_array, shape = [1] if n_classes == 2 else [n_classes]
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Constants in decision function.
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n_iter_int
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The actual number of iterations to reach the stopping criterion.
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t_int
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Number of weight updates performed during training. Same as
(n_iter_ * n_samples)
.
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See also
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]
© 2007–2020 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html