class sklearn.kernel_ridge.KernelRidge(alpha=1, kernel=’linear’, gamma=None, degree=3, coef0=1, kernel_params=None)
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Kernel ridge regression.
Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For non-linear kernels, this corresponds to a non-linear 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 epsilon-insensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closed-form and is typically faster for medium-sized datasets. On the other hand, the learned model is non-sparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at prediction-time.
This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape [n_samples, n_targets]).
Read more in the User Guide.
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See also
sklearn.linear_model.Ridge
sklearn.svm.SVR
>>> 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) KernelRidge(alpha=1.0, coef0=1, degree=3, gamma=None, kernel='linear', kernel_params=None)
fit (X[, y, sample_weight]) | Fit Kernel Ridge regression model |
get_params ([deep]) | Get parameters for this estimator. |
predict (X) | Predict using the kernel ridge model |
score (X, y[, sample_weight]) | Returns the coefficient of determination R^2 of the prediction. |
set_params (**params) | Set the parameters of this estimator. |
__init__(alpha=1, kernel=’linear’, gamma=None, degree=3, coef0=1, kernel_params=None)
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fit(X, y=None, sample_weight=None)
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Fit Kernel Ridge regression model
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get_params(deep=True)
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Get parameters for this estimator.
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predict(X)
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Predict using the kernel ridge model
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score(X, y, sample_weight=None)
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Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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sklearn.kernel_ridge.KernelRidge
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.kernel_ridge.KernelRidge.html