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.

class node_regression.SupportVectorRegression

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