Epsilon Support Vector Regression

Support vector machine based regressor (SVR)

Documentation

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’.

  Current default is ‘auto’ which uses 1 / n_features, if gamma='scale' is passed then it uses 1 / (n_features * X.var()) 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 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.

Input ports:

Output ports:

modelmodel

Model

Definition

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