18797
18336
sklearn.svm._classes.SVC
sklearn.SVC
sklearn.svm._classes.SVC
8
openml==0.12.0,sklearn==0.24.1
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.svm.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`.
2021-04-12T09:54:01
English
sklearn==0.24.1
numpy>=1.13.3
scipy>=0.19.1
joblib>=0.11
threadpoolctl>=2.0.0
C
float
0.559956366390858
Regularization parameter. The strength of the regularization is
inversely proportional to C. Must be strictly positive. The penalty
is a squared l2 penalty
kernel : {'linear', 'poly', 'rbf', 'sigmoid', 'precomputed'}, 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)``
break_ties
bool
false
If true, ``decision_function_shape='ovr'``, and number of classes > 2,
:term:`predict` will break ties according to the confidence values of
:term:`decision_function`; otherwise the first class among the tied
classes is returned. Please note that breaking ties comes at a
relatively high computational cost compared to a simple predict
.. versionadded:: 0.22
cache_size
float
200
Specify the size of the kernel cache (in MB)
class_weight
dict or
null
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))``
coef0
float
0.12024401645987903
Independent term in kernel function
It is only significant in 'poly' and 'sigmoid'
decision_function_shape
"ovr"
degree
int
3
Degree of the polynomial kernel function ('poly')
Ignored by all other kernels
gamma : {'scale', 'auto'} or float, default='scale'
Kernel coefficient for 'rbf', 'poly' and 'sigmoid'
- if ``gamma='scale'`` (default) is passed then it uses
1 / (n_features * X.var()) as value of gamma,
- if 'auto', uses 1 / n_features
.. versionchanged:: 0.22
The default value of ``gamma`` changed from 'auto' to 'scale'
gamma
0.0004899581039164595
kernel
"sigmoid"
max_iter
int
-1
Hard limit on iterations within solver, or -1 for no limit
decision_function_shape : {'ovo', 'ovr'}, default='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. The parameter is
ignored for binary classification
.. 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*
probability
bool
false
Whether to enable probability estimates. This must be enabled prior
to calling `fit`, will slow down that method as it internally uses
5-fold cross-validation, and `predict_proba` may be inconsistent with
`predict`. Read more in the :ref:`User Guide <scores_probabilities>`
random_state
int
null
Controls the pseudo random number generation for shuffling the data for
probability estimates. Ignored when `probability` is False
Pass an int for reproducible output across multiple function calls
See :term:`Glossary <random_state>`.
shrinking
bool
false
Whether to use the shrinking heuristic
See the :ref:`User Guide <shrinking_svm>`
tol
float
0.00015812804979084444
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_0.24.1