.. _`Kernel Ridge Regression`: .. _`org.sysess.sympathy.machinelearning.kernel_ridge`: Kernel Ridge Regression ~~~~~~~~~~~~~~~~~~~~~~~ .. image:: kernel_ridge.svg :width: 48 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]. *Input ports*: *Output ports*: **model** : model Model **alpha** (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) 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) 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** (coef0) Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels. **degree** (degree) Degree of the polynomial kernel. Ignored by other kernels. .. automodule:: node_regression .. class:: KernelRidge