8000 DOC svm add callable as accepted type for kernel parameter (#21675) · scikit-learn/scikit-learn@51174b7 · GitHub
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DOC svm add callable as accepted type for kernel parameter (#21675)
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‎sklearn/svm/_classes.py

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@@ -538,10 +538,9 @@ class SVC(BaseSVC):
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inversely proportional to C. Must be strictly positive. The penalty
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is a squared l2 penalty.
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
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default='rbf'
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Specifies the kernel type to be used in the algorithm.
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It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
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a callable.
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If none is given, 'rbf' will be used. If a callable is given it is
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used to pre-compute the kernel matrix from data matrices; that matrix
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should be an array of shape ``(n_samples, n_samples)``.
@@ -802,10 +801,9 @@ class NuSVC(BaseSVC):
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<nu_svc>`) and a lower bound of the fraction of support vectors.
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Should be in the interval (0, 1].
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
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default='rbf'
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Specifies the kernel type to be used in the algorithm.
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It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
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a callable.
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If none is given, 'rbf' will be used. If a callable is given it is
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used to precompute the kernel matrix.
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@@ -1067,10 +1065,9 @@ class SVR(RegressorMixin, BaseLibSVM):
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Parameters
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----------
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
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default='rbf'
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Specifies the kernel type to be used in the algorithm.
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It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
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a callable.
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If none is given, 'rbf' will be used. If a callable is given it is
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used to precompute the kernel matrix.
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@@ -1271,10 +1268,9 @@ class NuSVR(RegressorMixin, BaseLibSVM):
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C : float, default=1.0
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Penalty parameter C of the error term.
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
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default='rbf'
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Specifies the kernel type to be used in the algorithm.
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It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
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a callable.
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If none is given, 'rbf' will be used. If a callable is given it is
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used to precompute the kernel matrix.
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@@ -1454,10 +1450,9 @@ class OneClassSVM(OutlierMixin, BaseLibSVM):
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Parameters
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----------
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, default='rbf'
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kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'} or callable, \
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default='rbf'
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Specifies the kernel type to be used in the algorithm.
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It must be one of 'linear', 'poly', 'rbf', 'sigmoid', 'precomputed' or
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a callable.
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If none is given, 'rbf' will be used. If a callable is given it is
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used to precompute the kernel matrix.
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