Visibility: public Uploaded 07-11-2019 by Matthias Feurer sklearn==0.21.2 numpy>=1.6.1 scipy>=0.9 29 runs
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  • openml-python python scikit-learn sklearn sklearn_0.21.2
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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.linear_model.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`.


CPenalty parameter C of the error termdefault: 0.5
cache_sizeSpecify 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))``default: 200
class_weightdefault: null
coef0Independent term in kernel function It is only significant in 'poly' and 'sigmoid'default: 0.0
decision_function_shapeWhether 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 .. 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: "ovr"
degreeDegree of the polynomial kernel function ('poly') Ignored by all other kernelsdefault: 3
gammaKernel coefficient for 'rbf', 'poly' and 'sigmoid' Current default is 'auto' which uses 1 / n_features, if ``gamma='scale'`` is passed then it uses 1 / (n_features * X.var()) as value of gamma. The current default of gamma, 'auto', will change to 'scale' in version 0.22. 'auto_deprecated', a deprecated version of 'auto' is used as a default indicating that no explicit value of gamma was passeddefault: "auto"
kernelSpecifies 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: "linear"
max_iterHard limit on iterations within solver, or -1 for no limitdefault: -1
probabilityWhether to enable probability estimates. This must be enabled prior to calling `fit`, and will slow down that methoddefault: true
random_stateThe seed of the pseudo random number generator used when shuffling the data for probability estimates. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`.default: null
shrinkingWhether to use the shrinking heuristicdefault: true
tolTolerance for stopping criteriondefault: 1e-05
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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