Epsilon Support Vector Regression

Support vector machine based regressor (SVR)

Documentation

Support vector machine based regressor (SVR)

Configuration:

• C

  Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.

• 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.

• 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.

• gamma

  Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.

  • if gamma='scale' (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,

  • if ‘auto’, uses 1 / n_features.

  Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.

• degree

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

• coef0

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

• max_iter

  Hard limit on iterations within solver, or -1 for no limit.

• tol

  Tolerance for stopping criterion.

• shrinking

  Whether to use the shrinking heuristic. See the User Guide <shrinking_svm>.

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

Input ports

Output ports

model

model

Model

class node_regression.SupportVectorRegression