`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 sklearn.svm.LinearSVC or sklearn.linear_model.SGDClassifier instead, possibly after a 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: Kernel functions.

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 comparison to regularizedlikelihood 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)	Evaluates the decision function for the samples in X.
`fit`(X, y[, sample_weight])	Fit the SVM model according to the given training data.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Perform classification on samples in X.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.

__init__(*, 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]¶: Initialize self. See help(type(self)) for accurate signature.

decision_function(X)[source]¶

Evaluates the decision function for the samples in X.

Parameters

Xarray-like of shape (n_samples, n_features)

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

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_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

paramsmapping of string to any: 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.

property predict_log_proba¶

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.

property predict_proba¶

Compute 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): 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.

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) wrt. y.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). 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

selfobject: Estimator instance.

Examples using `sklearn.svm.SVC`¶

Release Highlights for scikit-learn 0.22¶

Recognizing hand-written digits¶

Plot classification probability¶

Classifier comparison¶

Plot the decision boundaries of a VotingClassifier¶

Faces recognition example using eigenfaces and SVMs¶

Libsvm GUI¶

Recursive feature elimination¶

Recursive feature elimination with cross-validation¶

Test with permutations the significance of a classification score¶

ROC Curve with Visualization API¶

Multilabel classification¶

Explicit feature map approximation for RBF kernels¶

Confusion matrix¶

Plotting Validation Curves¶

Parameter estimation using grid search with cross-validation¶

Receiver Operating Characteristic (ROC) with cross validation¶

Nested versus non-nested cross-validation¶

Receiver Operating Characteristic (ROC)¶

Plotting Learning Curves¶

Concatenating multiple feature extraction methods¶

Feature discretization¶

Decision boundary of label propagation versus SVM on the Iris dataset¶

SVM: Maximum margin separating hyperplane¶

SVM with custom kernel¶

SVM Tie Breaking Example¶

SVM: Weighted samples¶

SVM: Separating hyperplane for unbalanced classes¶

SVM-Kernels¶

SVM-Anova: SVM with univariate feature selection¶

SVM Margins Example¶

Plot different SVM classifiers in the iris dataset¶

RBF SVM parameters¶

Cross-validation on Digits Dataset Exercise¶

SVM Exercise¶

sklearn.svm.SVC¶

Examples using sklearn.svm.SVC¶

`sklearn.svm`.SVC¶

Examples using `sklearn.svm.SVC`¶