Visibility: public Uploaded 03-01-2021 by Munum Butt sklearn==0.24.0 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.24.0
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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 :class:`~sklearn.svm.LinearSVC` or :class:`~sklearn.linear_model.SGDClassifier` instead, possibly after a :class:`~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: :ref:`svm_kernels`.


CRegularization 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)``default: 1.0
break_tiesIf true, ``decision_function_shape='ovr'``, and number of classes > 2, :term:`predict` will break ties according to the confidence values of :term:`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 .. versionadded:: 0.22default: false
cache_sizeSpecify the size of the kernel cache (in MB)default: 200
class_weightSet 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))``default: null
coef0Independent term in kernel function It is only significant in 'poly' and 'sigmoid'default: 0.0
decision_function_shapedefault: "ovr"
degreeDegree 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 .. versionchanged:: 0.22 The default value of ``gamma`` changed from 'auto' to 'scale'default: 3
gammadefault: "scale"
kerneldefault: "rbf"
max_iterHard 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 .. versionchanged:: 0.19 decision_function_shape is 'ovr' by default .. versionadded:: 0.17 *decision_function_shape='ovr'* is recommended .. versionchanged:: 0.17 Deprecated *decision_function_shape='ovo' and None*default: -1
probabilityWhether 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 :ref:`User Guide `default: false
random_stateControls 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 :term:`Glossary `.default: 54876
shrinkingWhether to use the shrinking heuristic See the :ref:`User Guide `default: true
tolTolerance for stopping criteriondefault: 0.001
verboseEnable 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 contextdefault: false


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