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[MRG] DOC: Add missing attributes to SVC and NuSVC #14930

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Merged
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Sep 13, 2019
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[MRG] DOC: Add missing attributes to SVC and NuSVC #14930

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d8772fd
Add missing attributes to SVC and NuSVC
kwinata Sep 9, 2019
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Apply suggestion: reformatting and rewording
kwinata Sep 13, 2019
0e339ef
rewording of probA_ and probB_ attributes docs
kwinata Sep 13, 2019
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Reformatting: adding bacticks
kwinata Sep 13, 2019
7be7aba
Merge branch 'master' into fix_missing_attributes
kwinata Sep 13, 2019
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remove trailing space
glemaitre Sep 13, 2019
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36 changes: 32 additions & 4 deletions sklearn/svm/classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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
Expand Down Expand Up @@ -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
Expand Down