Flow
sklearn.svm._classes.SVR

sklearn.svm._classes.SVR

Visibility: public Uploaded 23-03-2021 by Tan Zheng sklearn==0.23.2 numpy>=1.6.1 scipy>=0.9 5 runs
0 likes downloaded by 0 people 0 issues 0 downvotes , 0 total downloads
  • openml-python python scikit-learn sklearn sklearn_0.23.2
Issue #Downvotes for this reason By


Loading wiki
Help us complete this description Edit
Epsilon-Support Vector Regression. The free parameters in the model are C and epsilon. 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 datasets with more than a couple of 10000 samples. For large datasets consider using :class:`sklearn.svm.LinearSVR` or :class:`sklearn.linear_model.SGDRegressor` instead, possibly after a :class:`sklearn.kernel_approximation.Nystroem` transformer.

Parameters

CRegularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive The penalty is a squared l2 penaltydefault: 1.0
cache_sizeSpecify the size of the kernel cache (in MB)default: 200
coef0Independent term in kernel function It is only significant in 'poly' and 'sigmoid'default: 0.0
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
epsilonEpsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function with points predicted within a distance epsilon from the actual valuedefault: 0.1
gammadefault: "scale"
kerneldefault: "rbf"
max_iterHard limit on iterations within solver, or -1 for no limit.default: -1
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

0
Runs

List all runs
Parameter:
Rendering chart
Rendering table