Support Vector Classifier
Support vector machine (SVM) based classifier
| Configuration: |
C
Penalty parameter C of the error term.
kernel
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).
degree
Degree of the polynomial kernel function (‘poly’).
Ignored by all other kernels.
gamma
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.
coef0
Independent term in kernel function.
It is only significant in ‘poly’ and ‘sigmoid’.
probability
Whether to enable probability estimates. This must be enabled prior
to calling fit, and will slow down that method.
shrinking
Whether to use the shrinking heuristic.
tol
Tolerance for stopping criterion.
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))
max_iter
Hard limit on iterations within solver, or -1 for no limit.
random_state
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.
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| Attributes: |
support_
Indices of support vectors.
support_vectors_
Support vectors.
n_support_
Number of support vectors for each class.
dual_coef_
Coefficients of the support vector in the decision function.
For multiclass, coefficient for all 1-vs-1 classifiers.
The layout of the coefficients in the multiclass case is somewhat
non-trivial. See the section about multi-class classification in the
SVM section of the User Guide for details.
coef_
Coefficients of the support vector in the decision function.
For multiclass, coefficient for all 1-vs-1 classifiers.
The layout of the coefficients in the multiclass case is somewhat
non-trivial. See the section about multi-class classification in the
SVM section of the User Guide for details.
intercept_
Constants in decision function.
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| Inputs: | |
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| Outputs: |
- model : model
Model
|
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- Output ports:
-
- Configuration:
- C
- Penalty parameter C of the error term.
- kernel
- 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).
- degree
- Degree of the polynomial kernel function (‘poly’).
Ignored by all other kernels.
- gamma
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.
- coef0
- Independent term in kernel function.
It is only significant in ‘poly’ and ‘sigmoid’.
- probability
- Whether to enable probability estimates. This must be enabled prior
to calling fit, and will slow down that method.
- shrinking
- Whether to use the shrinking heuristic.
- tol
- Tolerance for stopping criterion.
- 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))
- max_iter
- Hard limit on iterations within solver, or -1 for no limit.
- random_state
- 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.
Some of the docstrings for this module have been automatically
extracted from the scikit-learn library
and are covered by their respective licenses.
-
class
node_svc.SupportVectorClassifier[source]
