8.26.1.1. sklearn.svm.SVC¶
- class sklearn.svm.SVC(C=None, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, scale_C=True, class_weight=None)¶
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.
Parameters : C : float or None, optional (default=None)
Penalty parameter C of the error term. If None then C is set to n_samples.
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’. If none is given, ‘rbf’ will be used.
degree : int, optional (default=3)
Degree of kernel function. It is significant only in ‘poly’ and ‘sigmoid’.
gamma : float, optional (default=0.0)
Kernel coefficient for ‘rbf’ and ‘poly’. 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 predict_proba.
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.
scale_C : bool, default: True
Scale C with number of samples. It makes the setting of C independent of the number of samples. To match libsvm commandline one should use scale_C=False. WARNING: scale_C will disappear in version 0.12.
See also
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=None, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.5, kernel='rbf', probability=False, scale_C=True, shrinking=True, tol=0.001) >>> print clf.predict([[-0.8, -1]]) [ 1.]
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. scaled_C_ float The C value passed to libsvm. Methods
decision_function(X) Distance of the samples X to the separating hyperplane. fit(X, y[, class_weight, sample_weight]) Fit the SVM model according to the given training data. get_params([deep]) Get parameters for the estimator predict(X) Perform classification or regression samples in X. predict_log_proba(X) Compute the log likehoods each possible outcomes of samples in X. predict_proba(X) Compute the likehoods each possible outcomes of samples in T. score(X, y) Returns the mean accuracy on the given test data and labels. set_params(**params) Set the parameters of the estimator. - __init__(C=None, kernel='rbf', degree=3, gamma=0.0, coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, scale_C=True, class_weight=None)¶
- decision_function(X)¶
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, class_weight=None, sample_weight=None)¶
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 (integers in classification, real numbers in regression)
sample_weight : array-like, shape = [n_samples], optional
Weights applied to individual samples (1. for unweighted).
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)¶
Get parameters for the estimator
Parameters : deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- predict(X)¶
Perform classification or regression samples in X.
For a classification model, the predicted class for each sample in X is returned. For a regression model, the function value of X calculated is returned.
For an one-class model, +1 or -1 is returned.
Parameters : X : {array-like, sparse matrix}, shape = [n_samples, n_features] Returns : C : array, shape = [n_samples]
- predict_log_proba(X)¶
Compute the log likehoods each possible outcomes of 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 : X : array-like, shape = [n_samples, n_classes]
Returns the log-probabilities of the sample for each class in the model, where classes are ordered by arithmetical order.
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.
- predict_proba(X)¶
Compute the likehoods each possible outcomes of samples in T.
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 : X : array-like, shape = [n_samples, n_classes]
Returns the probability of the sample for each class in the model, where classes are ordered by arithmetical order.
Notes
The probability model is created using cross validation, so the results can be slightly different than those obtained by predict. Also, it will meaningless results on very small datasets.
- score(X, y)¶
Returns the mean accuracy on the given test data and labels.
Parameters : X : array-like, shape = [n_samples, n_features]
Training set.
y : array-like, shape = [n_samples]
Labels for X.
Returns : z : float
- set_params(**params)¶
Set the parameters of the 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 :