| C | 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'} or callable, default='rbf'
Specifies the kernel type to be used in the algorithm
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)`` | default: 1.0 |
| break_ties | 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 | default: false |
| cache_size | Specify the size of the kernel cache (in MB) | default: 200 |
| class_weight | 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))`` | default: null |
| coef0 | Independent term in kernel function
It is only significant in 'poly' and 'sigmoid' | default: 0.0 |
| decision_function_shape | | default: "ovr" |
| degree | Degree of the polynomial kernel function ('poly')
Must be non-negative. 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
- if float, must be non-negative
.. versionchanged:: 0.22
The default value of ``gamma`` changed from 'auto' to 'scale' | default: 3 |
| gamma | | default: "scale" |
| kernel | | default: "rbf" |
| max_iter | 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, note that
internally, one-vs-one ('ovo') is always used as a multi-class strategy
to train models; an ovr matrix is only constructed from the ovo matrix
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* | default: -1 |
| probability | 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 ` | default: false |
| random_state | 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 `. | default: null |
| shrinking | Whether to use the shrinking heuristic
See the :ref:`User Guide ` | default: true |
| tol | Tolerance for stopping criterion | default: 0.001 |
| verbose | 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 | default: false |