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. 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. Read more in the User Guide.  Parameters 
 
Cfloat, default=1.0 

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’}, default=’rbf’ 

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).  
degreeint, default=3 

Degree of the polynomial kernel function (‘poly’). 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.   Changed in version 0.22: The default value of gamma 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 the User Guide.  
probabilitybool, default=False 

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 User 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 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))  
verbosebool, default=False 

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.  
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 original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one (‘ovo’) is always used as multi-class strategy. The parameter is ignored for binary classification.  Changed in version 0.19: decision_function_shape is ‘ovr’ by default.   New in version 0.17: decision_function_shape=’ovr’ is recommended.   Changed in version 0.17: Deprecated decision_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 of 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.  New in version 0.22.   
random_stateint, RandomState instance or None, default=None 

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 Glossary.    Attributes 
 
class_weight_ndarray of shape (n_classes,) 

Multipliers of parameter C for each class. Computed based on the class_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 (coefficients in the primal problem). This is only available in the case of a linear kernel. coef_ is a readonly property derived from dual_coef_ and support_vectors_.  
dual_coef_ndarray of shape (n_classes -1, n_SV) 

Dual coefficients of the support vector in the decision function (see Mathematical formulation), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the multi-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.  
support_ndarray of shape (n_SV) 

Indices of support vectors.  
support_vectors_ndarray of shape (n_SV, n_features) 

Support vectors.  
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) 

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

If probability=True, it corresponds to the parameters learned in Platt scaling to produce probability estimates from decision values. If probability=False, it’s an empty array. Platt scaling uses the logistic function 1 / (1 + exp(decision_value * probA_ + probB_)) where probA_ and probB_ are learned from the dataset [2]. For more information on the multiclass case and training procedure see section 8 of [1].  
shape_fit_tuple of int of shape (n_dimensions_of_X,) 

Array dimensions of training vector X.      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.    
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 
 
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.     
  
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 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.     
 
 Examples using sklearn.svm.SVC
 
  Release Highlights for scikit-learn 0.24  

  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  

  Scalable learning with polynomial kernel aproximation  

  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  

  Comparison between grid search and successive halving  

  Receiver Operating Characteristic (ROC)  

  Plotting Learning Curves  

  Statistical comparison of models using grid search  

  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  

  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