8000 [WIP] DOC documentation for default values in linear_models by shahules786 · Pull Request #14505 · scikit-learn/scikit-learn · GitHub
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[WIP] DOC documentation for default values in linear_models #14505

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documented default values
shahules786 Jul 29, 2019
7e4d410
documented default values
shahules786 Jul 29, 2019
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16 changes: 8 additions & 8 deletions sklearn/linear_model/passive_aggressive.py
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Expand Up @@ -61,7 +61,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
verbose : integer, optional
The verbosity level

loss : string, optional
loss : string, optional (default="hinge")
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.
Expand All @@ -80,7 +80,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
generator; If None, the random number generator is the RandomState
instance used by `np.random`.

warm_start : bool, optional
warm_start : bool, optional (default=False)
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
See :term:`the Glossary <warm_start>`.
Expand All @@ -90,7 +90,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
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.
Preset for the class_weight fit parameter. (default=None)

Weights associated with classes. If not given, all classes
are supposed to have weight one.
Expand All @@ -102,7 +102,7 @@ class PassiveAggressiveClassifier(BaseSGDClassifier):
.. versionadded:: 0.17
parameter *class_weight* to automatically weight samples.

average : bool or int, optional
average : bool or int, optional (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
Expand Down Expand Up @@ -306,10 +306,10 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
shuffle : bool, default=True
Whether or not the training data should be shuffled after each epoch.

verbose : integer, optional
verbose : integer, optional (default=0)
The verbosity level

loss : string, optional
loss : string, optional (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
Expand All @@ -326,7 +326,7 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
generator; If None, the random number generator is the RandomState
instance used by `np.random`.

warm_start : bool, optional
warm_start : bool, optional (default=False)
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
See :term:`the Glossary <warm_start>`.
Expand All @@ -335,7 +335,7 @@ class PassiveAggressiveRegressor(BaseSGDRegressor):
result in a different solution than when calling fit a single time
because of the way the data is shuffled.

average : bool or int, optional
average : bool or int, optional (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
Expand Down
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