18862 6691 sklearn.svm.classes.SVC sklearn.SVC sklearn.svm.classes.SVC 45 openml==0.12.2,sklearn==0.18 C-Support Vector Classification. The implementation is 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 other, see the corresponding section in the narrative documentation: :ref:`svm_kernels`. 2021-08-13T17:57:37 English sklearn==0.18 numpy>=1.6.1 scipy>=0.9 C float 1.0 Penalty parameter C of the error term cache_size float 200 Specify the size of the kernel cache (in MB) class_weight : {dict, 'balanced'}, 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 "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))`` class_weight null coef0 float 0.0 Independent term in kernel function It is only significant in 'poly' and 'sigmoid' decision_function_shape 'ovo' null 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) The default of None will currently behave as 'ovo' for backward compatibility and raise a deprecation warning, but will change 'ovr' in 0.19 .. versionadded:: 0.17 *decision_function_shape='ovr'* is recommended .. versionchanged:: 0.17 Deprecated *decision_function_shape='ovo' and None* degree int 3 Degree of the polynomial kernel function ('poly') Ignored by all other kernels gamma float "auto" Kernel coefficient for 'rbf', 'poly' and 'sigmoid' If gamma is 'auto' then 1/n_features will be used instead kernel string "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)`` max_iter int -1 Hard limit on iterations within solver, or -1 for no limit probability boolean false Whether to enable probability estimates. This must be enabled prior to calling `fit`, and will slow down that method random_state int seed null The seed of the pseudo random number generator to use when shuffling the data for probability estimation. shrinking boolean true Whether to use the shrinking heuristic tol float 0.001 Tolerance for stopping criterion verbose bool 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 openml-python python scikit-learn sklearn sklearn_0.18