17445
1
sklearn.svm.classes.SVC
sklearn.SVC
sklearn.svm.classes.SVC
39
openml==0.10.2,sklearn==0.21.3
C-Support Vector Classification.
The implementation is based on libsvm. The fit time scales at least
quadratically with the number of samples and may be impractical
beyond tens of thousands of samples. For large datasets
consider using :class:`sklearn.linear_model.LinearSVC` or
:class:`sklearn.linear_model.SGDClassifier` instead, possibly after a
:class:`sklearn.kernel_approximation.Nystroem` transformer.
The multiclass support is handled according to a one-vs-one scheme.
For details on the precise mathematical formulation of the provided
kernel functions and how `gamma`, `coef0` and `degree` affect each
other, see the corresponding section in the narrative documentation:
:ref:`svm_kernels`.
2019-11-25T09:52:34
English
sklearn==0.21.3
numpy>=1.6.1
scipy>=0.9
C
float
1.0
Penalty parameter C of the error term
cache_size
float
200
Specify the size of the kernel cache (in MB)
class_weight : {dict, 'balanced'}, optional
Set the parameter C of class i to class_weight[i]*C for
SVC. If not given, all classes are supposed to have
weight one
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
class_weight
null
coef0
float
0.0
Independent term in kernel function
It is only significant in 'poly' and 'sigmoid'
decision_function_shape
'ovo'
"ovr"
Whether to return a one-vs-rest ('ovr') decision function of shape
(n_samples, n_classes) as all other classifiers, or the original
one-vs-one ('ovo') decision function of libsvm which has shape
(n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one
('ovo') is always used as multi-class strategy
.. versionchanged:: 0.19
decision_function_shape is 'ovr' by default
.. versionadded:: 0.17
*decision_function_shape='ovr'* is recommended
.. versionchanged:: 0.17
Deprecated *decision_function_shape='ovo' and None*
degree
int
3
Degree of the polynomial kernel function ('poly')
Ignored by all other kernels
gamma
float
"auto_deprecated"
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'
Current default is 'auto' which uses 1 / n_features,
if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.var())
as value of gamma. The current default of gamma, 'auto', will change
to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of
'auto' is used as a default indicating that no explicit value of gamma
was passed
kernel
string
"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)``
max_iter
int
-1
Hard limit on iterations within solver, or -1 for no limit
probability
boolean
false
Whether to enable probability estimates. This must be enabled prior
to calling `fit`, and will slow down that method
random_state
int
0
The seed of the pseudo random number generator used when shuffling
the data for probability estimates. If int, random_state is the
seed used by the random number generator; If RandomState instance,
random_state is the random number generator; If None, the random
number generator is the RandomState instance used by `np.random`.
shrinking
boolean
true
Whether to use the shrinking heuristic
tol
float
0.001
Tolerance for stopping criterion
verbose
bool
false
Enable verbose output. Note that this setting takes advantage of a
per-process runtime setting in libsvm that, if enabled, may not work
properly in a multithreaded context
openml-python
python
scikit-learn
sklearn
sklearn_0.21.3