sklearn.kernel_ridge
.KernelRidge¶
- class sklearn.kernel_ridge.KernelRidge(alpha=1, *, kernel='linear', gamma=None, degree=3, coef0=1, kernel_params=None)[source]¶
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.
- Parameters:
- alphafloat or array-like of shape (n_targets,), default=1.0
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 and classification for formula.- kernelstr or callable, default=”linear”
Kernel mapping used internally. This parameter is directly passed to
pairwise_kernel
. Ifkernel
is a string, it must be one of the metrics inpairwise.PAIRWISE_KERNEL_FUNCTIONS
or “precomputed”. Ifkernel
is “precomputed”, X is assumed to be a kernel matrix. Alternatively, ifkernel
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.- gammafloat, 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.
- degreefloat, default=3
Degree of the polynomial kernel. Ignored by other kernels.
- coef0float, default=1
Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.
- kernel_paramsmapping of str to any, default=None
Additional parameters (keyword arguments) for kernel function passed as callable object.
- Attributes:
- dual_coef_ndarray of shape (n_samples,) or (n_samples, n_targets)
Representation of weight vector(s) in kernel space
- X_fit_{ndarray, sparse matrix} of shape (n_samples, n_features)
Training data, which is also required for prediction. If kernel == “precomputed” this is instead the precomputed training matrix, of shape (n_samples, n_samples).
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
See also
sklearn.gaussian_process.GaussianProcessRegressor
Gaussian Process regressor providing automatic kernel hyperparameters tuning and predictions uncertainty.
sklearn.linear_model.Ridge
Linear ridge regression.
sklearn.linear_model.RidgeCV
Ridge regression with built-in cross-validation.
sklearn.svm.SVR
Support Vector Regression accepting a large variety of kernels.
References
Kevin P. Murphy “Machine Learning: A Probabilistic Perspective”, The MIT Press chapter 14.4.3, pp. 492-493
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) >>> krr = KernelRidge(alpha=1.0) >>> krr.fit(X, y) KernelRidge(alpha=1.0)
Methods
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])Return the coefficient of determination of the prediction.
set_params
(**params)Set the parameters of this estimator.
- fit(X, y, sample_weight=None)[source]¶
Fit Kernel Ridge regression model.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data. If kernel == “precomputed” this is instead a precomputed kernel matrix, of shape (n_samples, n_samples).
- yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values.
- sample_weightfloat or array-like of shape (n_samples,), default=None
Individual weights for each sample, ignored if None is passed.
- Returns:
- selfobject
Returns the instance itself.
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters:
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns:
- paramsdict
Parameter names mapped to their values.
- predict(X)[source]¶
Predict using the kernel ridge model.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Samples. If kernel == “precomputed” this is instead a precomputed kernel matrix, shape = [n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for this estimator.
- Returns:
- Cndarray of shape (n_samples,) or (n_samples, n_targets)
Returns predicted values.
- score(X, y, sample_weight=None)[source]¶
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), 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 ofy
, disregarding the input features, would get a \(R^2\) score of 0.0.- Parameters:
- Xarray-like of shape (n_samples, n_features)
Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape
(n_samples, n_samples_fitted)
, wheren_samples_fitted
is the number of samples used in the fitting for the estimator.- yarray-like of shape (n_samples,) or (n_samples, n_outputs)
True values for
X
.- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- scorefloat
\(R^2\) of
self.predict(X)
wrt.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline
). The latter have parameters of the form<component>__<parameter>
so that it’s possible to update each component of a nested object.- Parameters:
- **paramsdict
Estimator parameters.
- Returns:
- selfestimator instance
Estimator instance.
Examples using sklearn.kernel_ridge.KernelRidge
¶
Comparison of kernel ridge and Gaussian process regression
Comparison of kernel ridge regression and SVR