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DOC Ensures that PassiveAggressiveRegressor passes numpydoc validation (#21413)
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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‎maint_tools/test_docstrings.py

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DOCSTRING_IGNORE_LIST = [
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"LabelSpreading",
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"MultiTaskElasticNetCV",
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"PassiveAggressiveRegressor",
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"SpectralCoclustering",
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"SpectralEmbedding",
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"StackingRegressor",

‎sklearn/linear_model/_passive_aggressive.py

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@@ -301,7 +301,7 @@ def fit(self, X, y, coef_init=None, intercept_init=None):
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class PassiveAggressiveRegressor(BaseSGDRegressor):
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"""Passive Aggressive Regressor
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"""Passive Aggressive Regressor.
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Read more in the :ref:`User Guide <passive_aggressive>`.
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shuffle : bool, default=True
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Whether or not the training data should be shuffled after each epoch.
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verbose : integer, default=0
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verbose : int, default=0
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The verbosity level.
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loss : str, default="epsilon_insensitive"
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Number of weight updates performed during training.
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Same as ``(n_iter_ * n_samples)``.
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See Also
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--------
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SGDRegressor : Linear model fitted by minimizing a regularized
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empirical loss with SGD.
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References
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----------
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Online Passive-Aggressive Algorithms
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<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
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K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006).
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Examples
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--------
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>>> from sklearn.linear_model import PassiveAggressiveRegressor
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[-0.02306214]
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>>> print(regr.predict([[0, 0, 0, 0]]))
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[-0.02306214]
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See Also
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--------
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SGDRegressor : Linear model fitted by minimizing a regularized
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empirical loss with SGD.
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References
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----------
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Online Passive-Aggressive Algorithms
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<http://jmlr.csail.mit.edu/papers/volume7/crammer06a/crammer06a.pdf>
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K. Crammer, O. Dekel, J. Keshat, S. Shalev-Shwartz, Y. Singer - JMLR (2006).
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"""
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def __init__(

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