Kernel Ridge Regression

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

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. Set to “precomputed” in order to pass a precomputed kernel matrix to the estimator methods instead of samples.

  • 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. If kernel == “precomputed” this is instead the precomputed training matrix, shape = [n_samples, n_samples].
Inputs:
Outputs:
model : model

Model
Output ports:
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. Set to “precomputed” in order to pass a precomputed kernel matrix to the estimator methods instead of samples.
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[source]