Kernel Ridge Regression¶
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 model
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 asLogisticRegression
orLinearSVC
. 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 fromsklearn.metrics.pairwise
are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead.
Implementation¶
- class node_regression.KernelRidge[source]