Epsilon Support Vector Regression

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

Ports:

Outputs:

model:

model

Model

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.

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

class node_regression.SupportVectorRegression