SVC#

classsklearn.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 leastquadratically with the number of samples and may be impracticalbeyond tens of thousands of samples. For large datasetsconsider usingLinearSVC orSGDClassifier instead, possibly after aNystroem transformer orotherKernel Approximation.

The multiclass support is handled according to a one-vs-one scheme.

For details on the precise mathematical formulation of the providedkernel functions and howgamma,coef0 anddegree affect eachother, 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

Read more in theUser Guide.

Parameters:
Cfloat, default=1.0

Regularization parameter. The strength of the regularization isinversely proportional to C. Must be strictly positive. The penaltyis a squared l2 penalty. For an intuitive visualization of the effectsof scaling the regularization parameter C, seeScaling the regularization parameter for SVCs.

kernel{‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’} or callable, default=’rbf’

Specifies the kernel type to be used in the algorithm. Ifnone is given, ‘rbf’ will be used. If a callable is given it is used topre-compute the kernel matrix from data matrices; that matrix should bean array of shape(n_samples,n_samples). For an intuitivevisualization of different kernel types seePlot classification boundaries with different SVM Kernels.

degreeint, default=3

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’.

  • ifgamma='scale' (default) is passed then it uses1 / (n_features * X.var()) as value of gamma,

  • if ‘auto’, uses 1 / n_features

  • if float, must be non-negative.

Changed in version 0.22:The default value ofgamma changed from ‘auto’ to ‘scale’.

coef0float, default=0.0

Independent term in kernel function.It is only significant in ‘poly’ and ‘sigmoid’.

shrinkingbool, default=True

Whether to use the shrinking heuristic.See theUser Guide.

probabilitybool, default=False

Whether to enable probability estimates. This must be enabled priorto callingfit, will slow down that method as it internally uses5-fold cross-validation, andpredict_proba may be inconsistent withpredict. Read more in theUser Guide.

tolfloat, default=1e-3

Tolerance for stopping criterion.

cache_sizefloat, default=200

Specify the size of the kernel cache (in MB).

class_weightdict or ‘balanced’, default=None

Set the parameter C of class i to class_weight[i]*C forSVC. If not given, all classes are supposed to haveweight one.The “balanced” mode uses the values of y to automatically adjustweights inversely proportional to class frequencies in the input dataasn_samples/(n_classes*np.bincount(y)).

verbosebool, default=False

Enable verbose output. Note that this setting takes advantage of aper-process runtime setting in libsvm that, if enabled, may not workproperly in a multithreaded context.

max_iterint, default=-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 originalone-vs-one (‘ovo’) decision function of libsvm which has shape(n_samples, n_classes * (n_classes - 1) / 2). However, note thatinternally, one-vs-one (‘ovo’) is always used as a multi-class strategyto train models; an ovr matrix is only constructed from the ovo matrix.The parameter is ignored for binary classification.

Changed in version 0.19:decision_function_shape is ‘ovr’ by default.

Added in version 0.17:decision_function_shape=’ovr’ is recommended.

Changed in version 0.17:Deprecateddecision_function_shape=’ovo’ and None.

break_tiesbool, default=False

If true,decision_function_shape='ovr', and number of classes > 2,predict will break ties according to the confidence values ofdecision_function; otherwise the first class among the tiedclasses is returned. Please note that breaking ties comes at arelatively high computational cost compared to a simple predict. SeeSVM Tie Breaking Example for anexample of its usage withdecision_function_shape='ovr'.

Added in version 0.22.

random_stateint, RandomState instance or None, default=None

Controls the pseudo random number generation for shuffling the data forprobability estimates. Ignored whenprobability is False.Pass an int for reproducible output across multiple function calls.SeeGlossary.

Attributes:
class_weight_ndarray of shape (n_classes,)

Multipliers of parameter C for each class.Computed based on theclass_weight parameter.

classes_ndarray of shape (n_classes,)

The classes labels.

coef_ndarray of shape (n_classes * (n_classes - 1) / 2, n_features)

Weights assigned to the features whenkernel="linear".

dual_coef_ndarray of shape (n_classes -1, n_SV)

Dual coefficients of the support vector in the decisionfunction (seeMathematical formulation), multiplied bytheir targets.For multiclass, coefficient for all 1-vs-1 classifiers.The layout of the coefficients in the multiclass case is somewhatnon-trivial. See themulti-class section of the User Guide for details.

fit_status_int

0 if correctly fitted, 1 otherwise (will raise warning)

intercept_ndarray of shape (n_classes * (n_classes - 1) / 2,)

Constants in decision function.

n_features_in_int

Number of features seen duringfit.

Added in version 0.24.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added in version 1.0.

n_iter_ndarray of shape (n_classes * (n_classes - 1) // 2,)

Number of iterations run by the optimization routine to fit the model.The shape of this attribute depends on the number of models optimizedwhich in turn depends on the number of classes.

Added in version 1.1.

support_ndarray of shape (n_SV)

Indices of support vectors.

support_vectors_ndarray of shape (n_SV, n_features)

Support vectors. An empty array if kernel is precomputed.

n_support_ndarray of shape (n_classes,), dtype=int32

Number of support vectors for each class.

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

Parameter learned in Platt scaling whenprobability=True.

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

Parameter learned in Platt scaling whenprobability=True.

shape_fit_tuple of int of shape (n_dimensions_of_X,)

Array dimensions of training vectorX.

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

Examples

>>>importnumpyasnp>>>fromsklearn.pipelineimportmake_pipeline>>>fromsklearn.preprocessingimportStandardScaler>>>X=np.array([[-1,-1],[-2,-1],[1,1],[2,1]])>>>y=np.array([1,1,2,2])>>>fromsklearn.svmimportSVC>>>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]

For a comparison of the SVC with other classifiers see:Plot classification probability.

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 classin the model.If decision_function_shape=’ovr’, the shape is (n_samples,n_classes).

Notes

If decision_function_shape=’ovo’, the function values are proportionalto the distance of the samples X to the separating hyperplane. If theexact distances are required, divide the function values by the norm ofthe weight vector (coef_). See alsothis question for further details.If decision_function_shape=’ovr’, the decision function is a monotonictransformation 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, wheren_samples is the number of samplesandn_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 inregression).

sample_weightarray-like of shape (n_samples,), default=None

Per-sample weights. Rescale C per sample. Higher weightsforce 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 andX 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 sparsematrices as input.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please checkUser Guide on how the routingmechanism works.

Returns:
routingMetadataRequest

AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator andcontained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

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 trainingtime: fit with attributeprobability 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 inthe model. The columns correspond to the classes in sortedorder, as they appear in the attributeclasses_.

Notes

The probability model is created using cross validation, sothe results can be slightly different than those obtained bypredict. Also, it will produce meaningless results on very smalldatasets.

predict_proba(X)[source]#

Compute probabilities of possible outcomes for samples in X.

The model needs to have probability information computed at trainingtime: fit with attributeprobability 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 inthe model. The columns correspond to the classes in sortedorder, as they appear in the attributeclasses_.

Notes

The probability model is created using cross validation, sothe results can be slightly different than those obtained bypredict. Also, it will produce meaningless results on very smalldatasets.

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

Returnaccuracy on provided data and labels.

In multi-label classification, this is the subset accuracywhich is a harsh metric since you require for each sample thateach 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 forX.

sample_weightarray-like of shape (n_samples,), default=None

Sample weights.

Returns:
scorefloat

Mean accuracy ofself.predict(X) w.r.t.y.

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

Configure whether metadata should be requested to be passed to thefit method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed tofit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it tofit.

  • 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter infit.

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 asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible 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]#

Configure whether metadata should be requested to be passed to thescore method.

Note that this method is only relevant when this estimator is used as asub-estimator within ameta-estimator and metadata routing is enabledwithenable_metadata_routing=True (seesklearn.set_config).Please check theUser Guide on how the routingmechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed toscore if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it toscore.

  • 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 theexisting request. This allows you to change the request for someparameters and not others.

Added in version 1.3.

Parameters:
sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing forsample_weight parameter inscore.

Returns:
selfobject

The updated object.

Gallery examples#

Faces recognition example using eigenfaces and SVMs

Faces recognition example using eigenfaces and SVMs

Classifier comparison

Classifier comparison

Recognizing hand-written digits

Recognizing hand-written digits

Concatenating multiple feature extraction methods

Concatenating multiple feature extraction methods

Scalable learning with polynomial kernel approximation

Scalable learning with polynomial kernel approximation

Explicit feature map approximation for RBF kernels

Explicit feature map approximation for RBF kernels

Multilabel classification

Multilabel classification

Displaying Pipelines

Displaying Pipelines

ROC Curve with Visualization API

ROC Curve with Visualization API

Confusion matrix

Confusion matrix

Custom refit strategy of a grid search with cross-validation

Custom refit strategy of a grid search with cross-validation

Statistical comparison of models using grid search

Statistical comparison of models using grid search

Plotting Learning Curves and Checking Models’ Scalability

Plotting Learning Curves and Checking Models' Scalability

Nested versus non-nested cross-validation

Nested versus non-nested cross-validation

Test with permutations the significance of a classification score

Test with permutations the significance of a classification score

Receiver Operating Characteristic (ROC) with cross validation

Receiver Operating Characteristic (ROC) with cross validation

Comparison between grid search and successive halving

Comparison between grid search and successive halving

Feature discretization

Feature discretization

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.22

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Effect of varying threshold for self-training

Effect of varying threshold for self-training

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

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

SVM with custom kernel

SVM with custom kernel

Plot different SVM classifiers in the iris dataset

Plot different SVM classifiers in the iris dataset

RBF SVM parameters

RBF SVM parameters

SVM: Maximum margin separating hyperplane

SVM: Maximum margin separating hyperplane

SVM: Separating hyperplane for unbalanced classes

SVM: Separating hyperplane for unbalanced classes

SVM-Anova: SVM with univariate feature selection

SVM-Anova: SVM with univariate feature selection

Plot classification boundaries with different SVM Kernels

Plot classification boundaries with different SVM Kernels

SVM Margins Example

SVM Margins Example

SVM Tie Breaking Example

SVM Tie Breaking Example

SVM: Weighted samples

SVM: Weighted samples