Kernel Ridge Regression

Kernel Ridge based classifier combining ridge regression (linear least-squares L2-norm) regression with the kernel trick

Configuration:

• alpha

  Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. Alpha corresponds to (2*C)^-1 in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.

• kernel

  Kernel mapping used internally. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number.

• gamma

  Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.

• coef0

  Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.

• degree

  Degree of the polynomial kernel. Ignored by other kernels.

Attributes:

• dual_coef_

  Representation of weight vector(s) in kernel space

• X_fit_

  Training data, which is also required for prediction

Inputs: Outputs:

model : model

Model

Ports:

Outputs:

model:

model

Model

Configuration:

alpha

Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. Alpha corresponds to (2*C)^-1 in other linear models such as LogisticRegression or LinearSVC. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number.

kernel

Kernel mapping used internally. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number.

gamma

Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels.

coef0

Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.

degree

Degree of the polynomial kernel. Ignored by other kernels.

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.KernelRidge