DOC Add missing attributes to SVC and NuSVC
@@ -584,15 +584,22 @@ class SVC(BaseSVC):
probA_ : array, shape = [n_class * (n_class-1) / 2]
probB_ : array, shape = [n_class * (n_class-1) / 2]
- If probability=True, the parameters learned in Platt scaling to
- produce probability estimates from decision values. If
- probability=False, an empty array. Platt scaling uses the logistic
- function
+ If `probability=True`, it corresponds to the parameters learned in
+ Platt scaling to produce probability estimates from decision values.
+ If `probability=False`, it's an empty array. Platt scaling uses the
+ logistic function
``1 / (1 + exp(decision_value * probA_ + probB_))``
where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
more information on the multiclass case and training procedure see
section 8 of [1]_.
+ class_weight_ : ndarray of shape (n_class,)
+ Multipliers of parameter C for each class.
+ Computed based on the ``class_weight`` parameter.
+
+ shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+ Array dimensions of training vector ``X``.
+
Examples
--------
>>> import numpy as np
@@ -778,6 +785,27 @@ class NuSVC(BaseSVC):
classes_ : array of shape = (n_classes,)
The unique classes labels.
+ fit_status_ : int
+ 0 if correctly fitted, 1 if the algorithm did not converge.
+
+ probA_ : ndarray, shape of (n_class * (n_class-1) / 2,)
+ probB_ : ndarray of shape (n_class * (n_class-1) / 2,)
+ If `probability=True`, it corresponds to the parameters learned in
+ Platt scaling to produce probability estimates from decision values.
+ If `probability=False`, it's an empty array. Platt scaling uses the
+ logistic function
+ ``1 / (1 + exp(decision_value * probA_ + probB_))``
+ where ``probA_`` and ``probB_`` are learned from the dataset [2]_. For
+ more information on the multiclass case and training procedure see
+ section 8 of [1]_.
+
+ class_weight_ : ndarray of shape (n_class,)
+ Multipliers of parameter C of each class.
+ Computed based on the ``class_weight`` parameter.
+
+ shape_fit_ : tuple of int of shape (n_dimensions_of_X,)
+ Array dimensions of training vector ``X``.
+
Examples
--------
>>> import numpy as np
