`sklearn.svm`.SVC#

class sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, random_state=None)[source]#

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 LinearSVC or SGDClassifier instead, possibly after a Nystroem transformer or other Kernel Approximation.

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: Kernel functions.

To learn how to tune SVC’s hyperparameters, see the following example: Nested versus non-nested cross-validation

See also

SVR: Support Vector Machine for Regression implemented using libsvm.
LinearSVC: Scalable Linear Support Vector Machine for classification implemented using liblinear. Check the See Also section of LinearSVC for more comparison element.

References

[1]

LIBSVM: A Library for Support Vector Machines

[2]

Platt, John (1999). “Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods”

Examples

>>> import numpy as np
>>> from sklearn.pipeline import make_pipeline
>>> from sklearn.preprocessing import StandardScaler
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import SVC
>>> clf = make_pipeline(StandardScaler(), SVC(gamma='auto'))
>>> clf.fit(X, y)
Pipeline(steps=[('standardscaler', StandardScaler()),
                ('svc', SVC(gamma='auto'))])

>>> print(clf.predict([[-0.8, -1]]))
[1]

Methods

`decision_function`(X)	Evaluate the decision function for the samples in X.
`fit`(X, y[, sample_weight])	Fit the SVM model according to the given training data.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Perform classification on samples in X.
`predict_log_proba`(X)	Compute log probabilities of possible outcomes for samples in X.
`predict_proba`(X)	Compute probabilities of possible outcomes for samples in X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, sample_weight])	Request metadata passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

property coef_#

Weights assigned to the features when kernel="linear".

Returns:

ndarray of shape (n_features, n_classes)

decision_function(X)[source]#

Evaluate the decision function for the samples in X.

Parameters:

Xarray-like of shape (n_samples, n_features): The input samples.

Returns:

Xndarray of shape (n_samples, n_classes * (n_classes-1) / 2): Returns the decision function of the sample for each class in the model. If decision_function_shape=’ovr’, the shape is (n_samples, n_classes).

Notes

If decision_function_shape=’ovo’, the function values are proportional to the distance of the samples X to the separating hyperplane. If the exact distances are required, divide the function values by the norm of the weight vector (coef_). See also this question for further details. If decision_function_shape=’ovr’, the decision function is a monotonic transformation of ovo decision function.

fit(X, y, sample_weight=None)[source]#

Fit the SVM model according to the given training data.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples, n_samples): Training vectors, where n_samples is the number of samples and n_features is the number of features. For kernel=”precomputed”, the expected shape of X is (n_samples, n_samples).
yarray-like of shape (n_samples,): Target values (class labels in classification, real numbers in regression).
sample_weightarray-like of shape (n_samples,), default=None: Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns:

selfobject: Fitted estimator.

Notes

If X and y are not C-ordered and contiguous arrays of np.float64 and X is not a scipy.sparse.csr_matrix, X and/or y may be copied.

If X is a dense array, then the other methods will not support sparse matrices as input.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

property n_support_#: Number of support vectors for each class.

predict(X)[source]#

Perform classification on samples in X.

For an one-class model, +1 or -1 is returned.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features) or (n_samples_test, n_samples_train): For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns:

y_predndarray of shape (n_samples,): Class labels for samples in X.

predict_log_proba(X)[source]#

Compute log probabilities of possible outcomes for samples in X.

The model need to have probability information computed at training time: fit with attribute probability set to True.

Parameters:

Xarray-like of shape (n_samples, n_features) or (n_samples_test, n_samples_train): For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns:

Tndarray of shape (n_samples, n_classes): Returns the log-probabilities of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

predict_proba(X)[source]#

Compute probabilities of possible outcomes for samples in X.

The model needs to have probability information computed at training time: fit with attribute probability set to True.

Parameters:

Xarray-like of shape (n_samples, n_features): For kernel=”precomputed”, the expected shape of X is (n_samples_test, n_samples_train).

Returns:

Tndarray of shape (n_samples, n_classes): Returns the probability of the sample for each class in the model. The columns correspond to the classes in sorted order, as they appear in the attribute classes_.

Notes

The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will produce meaningless results on very small datasets.

property probA_#

Parameter learned in Platt scaling when probability=True.

Returns:

ndarray of shape (n_classes * (n_classes - 1) / 2)

property probB_#

Parameter learned in Platt scaling when probability=True.

Returns:

ndarray of shape (n_classes * (n_classes - 1) / 2)

score(X, y, sample_weight=None)[source]#

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. y.

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SVC[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

set_params(**params)[source]#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → SVC[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Examples using `sklearn.svm.SVC`#

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.22

Classifier comparison

Plot classification probability

Recognizing hand-written digits

Plot the decision boundaries of a VotingClassifier

Faces recognition example using eigenfaces and SVMs

Libsvm GUI

Recursive feature elimination

Scalable learning with polynomial kernel approximation

Displaying Pipelines

Explicit feature map approximation for RBF kernels

Multilabel classification

ROC Curve with Visualization API

Comparison between grid search and successive halving

Confusion matrix

Custom refit strategy of a grid search with cross-validation

Nested versus non-nested cross-validation

Plotting Learning Curves and Checking Models’ Scalability

Plotting Learning Curves and Checking Models' Scalability

Plotting Validation Curves

Receiver Operating Characteristic (ROC) with cross validation

Statistical comparison of models using grid search

Test with permutations the significance of a classification score

Concatenating multiple feature extraction methods

Feature discretization

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

Effect of varying threshold for self-training

Plot classification boundaries with different SVM Kernels

Plot different SVM classifiers in the iris dataset

RBF SVM parameters

SVM Margins Example

SVM Tie Breaking Example

SVM with custom kernel

SVM-Anova: SVM with univariate feature selection

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

SVM: Weighted samples

SVM Exercise

sklearn.svm.SVC#

Examples using sklearn.svm.SVC#

`sklearn.svm`.SVC#

Examples using `sklearn.svm.SVC`#