Home  Trees  Indices  Help 



Kernel ridge regression. This node has been automatically generated by wrapping the ``sklearn.kernel_ridge.KernelRidge`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For nonlinear kernels, this corresponds to a nonlinear function in the original space. The form of the model learned by KRR is identical to support vector regression (SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses epsiloninsensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closedform and is typically faster for mediumsized datasets. On the other hand, the learned model is nonsparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at predictiontime. This estimator has builtin support for multivariate regression (i.e., when y is a 2darray of shape [n_samples, n_targets]). Read more in the :ref:`User Guide <kernel_ridge>`. **Parameters** alpha : {float, arraylike}, shape = [n_targets] 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 : string or callable, default="linear" 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. gamma : float, default=None 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. degree : float, default=3 Degree of the polynomial kernel. Ignored by other kernels. coef0 : float, default=1 Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels. kernel_params : mapping of string to any, optional Additional parameters (keyword arguments) for kernel function passed as callable object. **Attributes** ``dual_coef_`` : array, shape = [n_features] or [n_targets, n_features] Weight vector(s) in kernel space ``X_fit_`` : {arraylike, sparse matrix}, shape = [n_samples, n_features] Training data, which is also required for prediction **References** * Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", The MIT Press chapter 14.4.3, pp. 492493 See also Ridge Linear ridge regression. SVR Support Vector Regression implemented using libsvm. **Examples** >>> from sklearn.kernel_ridge import KernelRidge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> clf = KernelRidge(alpha=1.0) >>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear', kernel_params=None)














Inherited from Inherited from 

Inherited from Cumulator  





Inherited from Node  


































































Inherited from 

Inherited from Node  

_train_seq List of tuples: 

dtype dtype 

input_dim Input dimensions 

output_dim Output dimensions 

supported_dtypes Supported dtypes 

Kernel ridge regression. This node has been automatically generated by wrapping the ``sklearn.kernel_ridge.KernelRidge`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For nonlinear kernels, this corresponds to a nonlinear function in the original space. The form of the model learned by KRR is identical to support vector regression (SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses epsiloninsensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closedform and is typically faster for mediumsized datasets. On the other hand, the learned model is nonsparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at predictiontime. This estimator has builtin support for multivariate regression (i.e., when y is a 2darray of shape [n_samples, n_targets]). Read more in the :ref:`User Guide <kernel_ridge>`. **Parameters** alpha : {float, arraylike}, shape = [n_targets] 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 : string or callable, default="linear" 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. gamma : float, default=None 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. degree : float, default=3 Degree of the polynomial kernel. Ignored by other kernels. coef0 : float, default=1 Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels. kernel_params : mapping of string to any, optional Additional parameters (keyword arguments) for kernel function passed as callable object. **Attributes** ``dual_coef_`` : array, shape = [n_features] or [n_targets, n_features] Weight vector(s) in kernel space ``X_fit_`` : {arraylike, sparse matrix}, shape = [n_samples, n_features] Training data, which is also required for prediction **References** * Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", The MIT Press chapter 14.4.3, pp. 492493 See also Ridge Linear ridge regression. SVR Support Vector Regression implemented using libsvm. **Examples** >>> from sklearn.kernel_ridge import KernelRidge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> clf = KernelRidge(alpha=1.0) >>> clf.fit(X, y) # doctest: +NORMALIZE_WHITESPACE KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear', kernel_params=None)




Predict using the the kernel ridge model This node has been automatically generated by wrapping the sklearn.kernel_ridge.KernelRidge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns



Fit Kernel Ridge regression model This node has been automatically generated by wrapping the sklearn.kernel_ridge.KernelRidge class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters
Returns self : returns an instance of self.

Home  Trees  Indices  Help 


Generated by Epydoc 3.0.1 on Tue Mar 8 12:39:48 2016  http://epydoc.sourceforge.net 