[MRG] Write consistent descriptions of the data attribute C in LinearSVC, LinearSVR, SVC and SVR
@@ -44,7 +44,8 @@ class LinearSVC(BaseEstimator, LinearClassifierMixin,
Tolerance for stopping criteria.
C : float, optional (default=1.0)
- Penalty parameter C of the error term.
+ Regularization parameter. The strength of the regularization is
+ inversely proportional to C. Must be strictly positive.
multi_class : string, 'ovr' or 'crammer_singer' (default='ovr')
Determines the multi-class strategy if `y` contains more than
@@ -273,8 +274,8 @@ class LinearSVR(LinearModel, RegressorMixin):
Tolerance for stopping criteria.
C : float, optional (default=1.0)
- Penalty parameter C of the error term. The penalty is a squared
- l2 penalty. The bigger this parameter, the less regularization is used.
+ Regularization parameter. The strength of the regularization is
+ inversely proportional to C. Must be strictly positive.
loss : string, optional (default='epsilon_insensitive')
Specifies the loss function. The epsilon-insensitive loss
@@ -454,7 +455,9 @@ class SVC(BaseSVC):
Parameters
----------
C : float, optional (default=1.0)
- Penalty parameter C of the error term.
+ Regularization parameter. The strength of the regularization is
+ inversely proportional to C. Must be strictly positive. The penalty
+ is a squared l2 penalty.
kernel : string, optional (default='rbf')
Specifies the kernel type to be used in the algorithm.
@@ -866,7 +869,9 @@ class SVR(BaseLibSVM, RegressorMixin):
Tolerance for stopping criterion.
C : float, optional (default=1.0)
- Penalty parameter C of the error term.
+ Regularization parameter. The strength of the regularization is
+ inversely proportional to C. Must be strictly positive.
+ The penalty is a squared l2 penalty.
epsilon : float, optional (default=0.1)
Epsilon in the epsilon-SVR model. It specifies the epsilon-tube
