Epsilon Support Vector Regression¶
Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.
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class
node_regression.
SupportVectorRegression
[source]¶ Support vector machine based regressor (SVR)
Configuration: C
Penalty parameter C of the error term.
kernel
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 precompute the kernel matrix.
gamma
Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’. If gamma is ‘auto’ then 1/n_features will be used instead.
epsilon
Epsilon 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 value.
coef0
Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
tol
Tolerance for stopping criterion.
degree
Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
shrinking
Whether to use the shrinking heuristic.
max_iter
Hard limit on iterations within solver, or -1 for no limit.
Attributes: support_
Indices of support vectors.
support_vectors_
Support vectors.
dual_coef_
Coefficients of the support vector in the decision function.
intercept_
Constants in decision function.
coef_
Coefficients of the support vector in the decision function.
Inputs: Outputs: - model : model
Model