Kernel Ridge Regression

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

Documentation

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

Configuration:

• alpha

  Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) 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. See ridge_regression for formula.

• kernel

  Kernel mapping used internally. This parameter is directly passed to pairwise_kernel. If kernel is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS. If kernel is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if kernel is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables from sklearn.metrics.pairwise are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.

• 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, of shape (n_samples, n_samples).

Input ports:

Output ports:

modelmodel

Model

Definition

Input ports

Output ports

model

model

Model

class node_regression.KernelRidge