Kernel Ridge Regression

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

Documentation

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).

Definition

Output ports

model
Type: model
Description: Model

Configuration

Alpha (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.

Zero coefficient (coef0)

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

Degree (degree)

Degree of the polynomial kernel. Ignored by other kernels.

Gamma (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.

Kernel (kernel)

Kernel mapping used internally. This parameter is directly passed to pairwise_kernels. If kernel is a string, it must be one of the metrics in pairwise.PAIRWISE_KERNEL_FUNCTIONS or “precomputed”. 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.

Examples

The node can be found in:

Implementation

class node_regression.KernelRidge[source]