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sklearn.svm.SVC

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

C-Support Vector Classification.

The implementations is a based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples.

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, see the corresponding section in the narrative documentation: Kernel functions.

Parameters:

C : float, optional (default=1.0)

Penalty parameter C of the error term.

kernel : string, optional (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 precompute the kernel matrix.

degree : int, optional (default=3)

Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

gamma : float, optional (default=0.0)

Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is 0.0 then 1/n_features will be used instead.

coef0 : float, optional (default=0.0)

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

probability: boolean, optional (default=False) :

Whether to enable probability estimates. This must be enabled prior to calling fit, and will slow down that method.

shrinking: boolean, optional (default=True) :

Whether to use the shrinking heuristic.

tol : float, optional (default=1e-3)

Tolerance for stopping criterion.

cache_size : float, optional

Specify the size of the kernel cache (in MB)

class_weight : {dict, ‘auto’}, optional

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 ‘auto’ mode uses the values of y to automatically adjust weights inversely proportional to class frequencies.

verbose : bool, 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_iter : int, optional (default=-1)

Hard limit on iterations within solver, or -1 for no limit.

random_state : int seed, RandomState instance, or None (default)

The seed of the pseudo random number generator to use when shuffling the data for probability estimation.

Attributes:

support_ : array-like, shape = [n_SV]

Index of support vectors.

support_vectors_ : array-like, shape = [n_SV, n_features]

Support vectors.

n_support_ : array-like, dtype=int32, shape = [n_class]

number of support vector for each class.

dual_coef_ : array, shape = [n_class-1, n_SV]

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_ : array, shape = [n_class-1, n_features]

Weights asigned to the features (coefficients in the primal problem). This is only available in the case of linear kernel.

coef_ is readonly property derived from dual_coef_ and support_vectors_

intercept_ : array, shape = [n_class * (n_class-1) / 2]

Constants in decision function.

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.

Examples

>>> import numpy as np
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> y = np.array([1, 1, 2, 2])
>>> from sklearn.svm import SVC
>>> clf = SVC()
>>> clf.fit(X, y) 
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
    gamma=0.0, kernel='rbf', max_iter=-1, probability=False,
    random_state=None, shrinking=True, tol=0.001, verbose=False)
>>> print(clf.predict([[-0.8, -1]]))
[1]

Methods

decision_function(X) Distance of the samples X to the separating hyperplane.
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]) Returns 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=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, verbose=False, max_iter=-1, random_state=None)[source]
decision_function(X)[source]

Distance of the samples X to the separating hyperplane.

Parameters:

X : array-like, shape = [n_samples, n_features]

Returns:

X : array-like, shape = [n_samples, n_class * (n_class-1) / 2]

Returns the decision function of the sample for each class in the model.

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

Fit the SVM model according to the given training data.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

y : array-like, shape (n_samples,)

Target values (class labels in classification, real numbers in regression)

sample_weight : array-like, shape (n_samples,)

Per-sample weights. Rescale C per sample. Higher weights force the classifier to put more emphasis on these points.

Returns:

self : object

Returns self.

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:

deep: boolean, optional :

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

Returns:

params : mapping 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}, shape = [n_samples, n_features]

Returns:

y_pred : array, shape = [n_samples]

Class labels for samples in X.

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:

X : array-like, shape = [n_samples, n_features]

Returns:

T : array-like, 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

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:

X : array-like, shape = [n_samples, n_features]

Returns:

T : array-like, 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]

Returns 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:

X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True labels for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.

Returns:

score : float

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 former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :

Examples using sklearn.svm.SVC

In many real-world examples, there are many ways to extract features from a dataset. Often it i...

This example simulates a multi-label document classification problem. The dataset is generated ...

An example illustrating the approximation of the feature map of an RBF kernel.

The dataset used in this example is a preprocessed excerpt of the "Labeled Faces in the Wild", ...

A simple graphical frontend for Libsvm mainly intended for didactic purposes. You can create da...

An example showing how the scikit-learn can be used to recognize images of hand-written digits.

Plot the classification probability for different classifiers. We use a 3 class dataset, and we...

A comparison of a several classifiers in scikit-learn on synthetic datasets. The point of this ...

A tutorial excercise using Cross-validation with an SVM on the Digits dataset.

A tutorial exercise for using different SVM kernels.

Simple usage of Pipeline that runs successively a univariate feature selection with anova and t...

A recursive feature elimination example showing the relevance of pixels in a digit classificati...

A recursive feature elimination example with automatic tuning of the number of features selecte...

In order to test if a classification score is significative a technique in repeating the classi...

An example showing univariate feature selection.

In this plot you can see the training scores and validation scores of an SVM for different valu...

This examples shows how a classifier is optimized by cross-validation, which is done using the ...

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...

Example of confusion matrix usage to evaluate the quality of the output of a classifier on the ...

Example of Receiver Operating Characteristic (ROC) metric to evaluate classifier output quality...

Example of Precision-Recall metric to evaluate classifier output quality.

On the left side the learning curve of a naive Bayes classifier is shown for the digits dataset...

Comparison for decision boundary generated on iris dataset between Label Propagation and SVM.

Plot the maximum margin separating hyperplane within a two-class separable dataset using a Supp...

Find the optimal separating hyperplane using an SVC for classes that are unbalanced.

Simple usage of Support Vector Machines to classify a sample. It will plot the decision surface...

Plot decision function of a weighted dataset, where the size of points is proportional to its w...

This example shows how to perform univariate feature before running a SVC (support vector class...

Comparison of different linear SVM classifiers on a 2D projection of the iris dataset. We only ...

A small value of `C` includes more/all the observations, allowing the margins to be calculated ...

Three different types of SVM-Kernels are displayed below. The polynomial and RBF are especially...

This example illustrates the effect of the parameters `gamma` and `C` of the rbf kernel SVM.

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