Kernel Ridge Regression

../../../../_images/kernel_ridge.svg

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[source]

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