Kernel Ridge 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.KernelRidge

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