# Epsilon Support Vector Regression¶

Support vector machine based regressor (SVR)

*Configuration*:

CPenalty parameter C of the error term.

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 precompute the kernel matrix.

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.std()) 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 passed.

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 value.

coef0Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.

tolTolerance for stopping criterion.

degreeDegree of the polynomial kernel function (‘poly’). Ignored by all other kernels.

shrinkingWhether to use the shrinking heuristic.

max_iterHard 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.

*Input ports*:

*Output ports*:**model**: model- Model

**C**(C)- Penalty parameter C of the error term.
**kernel**(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**(gamma)Kernel 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.std()) 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 passed.**epsilon**(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**(coef0)- Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
**tol**(tol)- Tolerance for stopping criterion.
**degree**(degree)- Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
**shrinking**(shrinking)- Whether to use the shrinking heuristic.
**max_iter**(max_iter)- Hard limit on iterations within solver, or -1 for no limit.

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.