18862
6691
sklearn.svm.classes.SVC
sklearn.SVC
sklearn.svm.classes.SVC
45
openml==0.12.2,sklearn==0.18
C-Support Vector Classification.
The implementation is based on libsvm. The fit time complexity
is more than quadratic with the number of samples which makes it hard
to scale to dataset with more than a couple of 10000 samples.
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`.
2021-08-13T17:57:37
English
sklearn==0.18
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'
null
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)
The default of None will currently behave as 'ovo' for backward
compatibility and raise a deprecation warning, but will change 'ovr'
in 0.19
.. 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"
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'
If gamma is 'auto' then 1/n_features will be used instead
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 seed
null
The seed of the pseudo random number generator to use when
shuffling the data for probability estimation.
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_0.18