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DOC svm add callable as accepted type for kernel parameter #21675

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Merged
merged 5 commits into from
Dec 23, 2021
Merged

DOC svm add callable as accepted type for kernel parameter #21675

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da2bd3b
DOC svm _classes add callable as accepted type for parameter 'kernel'
mandjevant Nov 15, 2021
810a4f6
Reposition sentence for linter.
mandjevant Nov 15, 2021
24e4fbe
DOC Remove redundant parameter description.
mandjevant Nov 25, 2021
af9e8b2
DOC remove redundant parameter declaration
mandjevant Nov 26, 2021
814c5ce
Merge branch 'scikit-learn:main' into svm_classes_kernel_docs
mandjevant Dec 23, 2021
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25 changes: 10 additions & 15 deletions sklearn/svm/_classes.py
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Original file line number Diff line number Diff line change
Expand Up @@ -538,10 +538,9 @@ class SVC(BaseSVC):
inversely proportional to C. Must be strictly positive. The penalty
is a squared l2 penalty.

kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
default='rbf'
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to pre-compute the kernel matrix from data matrices; that matrix
should be an array of shape ``(n_samples, n_samples)``.
Expand Down Expand Up @@ -802,10 +801,9 @@ class NuSVC(BaseSVC):
<nu_svc>`) and a lower bound of the fraction of support vectors.
Should be in the interval (0, 1].

kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
default='rbf'
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.

Expand Down Expand Up @@ -1067,10 +1065,9 @@ class SVR(RegressorMixin, BaseLibSVM):

Parameters
----------
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
default='rbf'
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.

Expand Down Expand Up @@ -1271,10 +1268,9 @@ class NuSVR(RegressorMixin, BaseLibSVM):
C : float, default=1.0
Penalty parameter C of the error term.

kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
default='rbf'
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.

Expand Down Expand Up @@ -1454,10 +1450,9 @@ class OneClassSVM(OutlierMixin, BaseLibSVM):

Parameters
----------
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
default='rbf'
Specifies the kernel type to be used in the algorithm.
It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
a callable.
If none is given, 'rbf' will be used. If a callable is given it is
used to precompute the kernel matrix.

Expand Down