Visibility: public Uploaded 11-11-2019 by Matthias Feurer sklearn==0.22.dev0 numpy>=1.6.1 scipy>=0.9 4 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_0.22.dev0
Issue #Downvotes for this reason By

Loading wiki
Help us complete this description Edit
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 penaltydefault: 0.5
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) 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 kernels gamma : {'scale', 'auto'} or float, optional (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: "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`, 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: 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


List all runs
Rendering chart
Rendering table