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fix default value for class LinearSVC #16077

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fix default value for class LinearSVC #16077

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24 changes: 12 additions & 12 deletions sklearn/svm/_classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,28 +26,28 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,

Parameters
----------
penalty : str, 'l1' or 'l2' (default='l2')
penalty : str, 'l1' or 'l2' default='l2'
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penalty : str, 'l1' or 'l2' default='l2'
penalty : {'l1', 'l2'}, default='l2'

Specifies the norm used in the penalization. The 'l2'
penalty is the standard used in SVC. The 'l1' leads to ``coef_``
vectors that are sparse.

loss : str, 'hinge' or 'squared_hinge' (default='squared_hinge')
loss : str, 'hinge' or 'squared_hinge', default='squared_hinge'
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loss : str, 'hinge' or 'squared_hinge', default='squared_hinge'
loss : {'hinge', 'squared_hinge'}, default='squared_hinge'

Specifies the loss function. 'hinge' is the standard SVM loss
(used e.g. by the SVC class) while 'squared_hinge' is the
square of the hinge loss.

dual : bool, (default=True)
dual : bool, default=True
Select the algorithm to either solve the dual or primal
optimization problem. Prefer dual=False when n_samples > n_features.

tol : float, optional (default=1e-4)
tol : float, default=1e-4
Tolerance for stopping criteria.

C : float, optional (default=1.0)
C : float, default=1.0
Regularization parameter. The strength of the regularization is
inversely proportional to C. Must be strictly positive.

multi_class : str, 'ovr' or 'crammer_singer' (default='ovr')
multi_class : str, 'ovr' or 'crammer_singer',default='ovr'
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multi_class : str, 'ovr' or 'crammer_singer',default='ovr'
multi_class : {'ovr' or 'crammer_singer'}, default='ovr'

Determines the multi-class strategy if `y` contains more than
two classes.
``"ovr"`` trains n_classes one-vs-rest classifiers, while
Expand All @@ -58,12 +58,12 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
If ``"crammer_singer"`` is chosen, the options loss, penalty and dual
will be ignored.

fit_intercept : bool, optional (default=True)
fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set
to false, no intercept will be used in calculations
(i.e. data is expected to be already centered).

intercept_scaling : float, optional (default=1)
intercept_scaling : float, default=1
When self.fit_intercept is True, instance vector x becomes
``[x, self.intercept_scaling]``,
i.e. a "synthetic" feature with constant value equals to
Expand All @@ -74,20 +74,20 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) intercept_scaling has to be increased.

class_weight : {dict, 'balanced'}, optional
class_weight : {dict, 'balanced'}, default=1
Set the parameter C of class i to ``class_weight[i]*C`` for
SVC. If not given, all classes are supposed to have
weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``.

verbose : int, (default=0)
verbose : int, default=0
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in liblinear that, if enabled, may not work
properly in a multithreaded context.

random_state : int, RandomState instance or None, optional (default=None)
random_state : int, RandomState instance or None, default=None
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random_state : int, RandomState instance or None, default=None
random_state : int, RandomState instance, default=None

The seed of the pseudo random number generator to use when shuffling
the data for the dual coordinate descent (if ``dual=True``). When
``dual=False`` the underlying implementation of :class:`LinearSVC`
Expand All @@ -97,7 +97,7 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
None, the random number generator is the RandomState instance used by
`np.random`.

max_iter : int, (default=1000)
max_iter : int, default=1000
The maximum number of iterations to be run.

Attributes
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