BayesianRidge#

class sklearn.linear_model.BayesianRidge(*, max_iter=300, tol=0.001, alpha_1=1e-06, alpha_2=1e-06, lambda_1=1e-06, lambda_2=1e-06, alpha_init=None, lambda_init=None, compute_score=False, fit_intercept=True, copy_X=True, verbose=False)[source]#

Bayesian ridge regression.

Fit a Bayesian ridge model. See the Notes section for details on this implementation and the optimization of the regularization parameters lambda (precision of the weights) and alpha (precision of the noise).

Read more in the User guide. For an intuitive visualization of how the sinusoid is approximated by a polynomial using different pairs of initial values, see Curve Fitting with Bayesian Ridge Regression.

Parameters:

max_iterint, default=300: Maximum number of iterations over the complete dataset before stopping independently of any early stopping criterion.

Changed in version 1.3.
tolfloat, default=1e-3: Stop the algorithm if w has converged.
alpha_1float, default=1e-6: Hyper-parameter : shape parameter for the gamma distribution prior over the alpha parameter.
alpha_2float, default=1e-6: Hyper-parameter : inverse scale parameter (rate parameter) for the gamma distribution prior over the alpha parameter.
lambda_1float, default=1e-6: Hyper-parameter : shape parameter for the gamma distribution prior over the lambda parameter.
lambda_2float, default=1e-6: Hyper-parameter : inverse scale parameter (rate parameter) for the gamma distribution prior over the lambda parameter.
alpha_initfloat, default=None: Initial value for alpha (precision of the noise). If not set, alpha_init is 1/Var(y).

Added in version 0.22.
lambda_initfloat, default=None: Initial value for lambda (precision of the weights). If not set, lambda_init is 1.

Added in version 0.22.
compute_scorebool, default=False: If True, compute the log marginal likelihood at each iteration of the optimization.
fit_interceptbool, default=True: Whether to calculate the intercept for this model. The intercept is not treated as a probabilistic parameter and thus has no associated variance. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).
copy_Xbool, default=True: If True, X will be copied; else, it may be overwritten.
verbosebool, default=False: Verbose mode when fitting the model.

Attributes:

coef_array-like of shape (n_features,): Coefficients of the regression model (mean of distribution)
intercept_float: Independent term in decision function. Set to 0.0 if fit_intercept = False.
alpha_float: Estimated precision of the noise.
lambda_float: Estimated precision of the weights.
sigma_array-like of shape (n_features, n_features): Estimated variance-covariance matrix of the weights
scores_array-like of shape (n_iter_+1,): If computed_score is True, value of the log marginal likelihood (to be maximized) at each iteration of the optimization. The array starts with the value of the log marginal likelihood obtained for the initial values of alpha and lambda and ends with the value obtained for the estimated alpha and lambda.
n_iter_int: The actual number of iterations to reach the stopping criterion.
X_offset_ndarray of shape (n_features,): If fit_intercept=True, offset subtracted for centering data to a zero mean. Set to np.zeros(n_features) otherwise.
X_scale_ndarray of shape (n_features,): Set to np.ones(n_features).
n_features_in_int: Number of features seen during fit.

Added 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.

Added in version 1.0.

See also

ARDRegression: Bayesian ARD regression.

Notes

There exist several strategies to perform Bayesian ridge regression. This implementation is based on the algorithm described in Appendix A of (Tipping, 2001) where updates of the regularization parameters are done as suggested in (MacKay, 1992). Note that according to A New View of Automatic Relevance Determination (Wipf and Nagarajan, 2008) these update rules do not guarantee that the marginal likelihood is increasing between two consecutive iterations of the optimization.

References

D. J. C. MacKay, Bayesian Interpolation, Computation and Neural Systems, Vol. 4, No. 3, 1992.

M. E. Tipping, Sparse Bayesian Learning and the Relevance Vector Machine, Journal of Machine Learning Research, Vol. 1, 2001.

Examples

&gt;&gt;&gt; from sklearn import linear_model
&gt;&gt;&gt; clf = linear_model.BayesianRidge()
&gt;&gt;&gt; clf.fit([[0,0], [1, 1], [2, 2]], [0, 1, 2])
BayesianRidge()
&gt;&gt;&gt; clf.predict([[1, 1]])
array([1.])

fit(X, y, sample_weight=None)[source]#

Fit the model.

Parameters:

Xndarray of shape (n_samples, n_features): Training data.
yndarray of shape (n_samples,): Target values. Will be cast to X’s dtype if necessary.
sample_weightndarray of shape (n_samples,), default=None: Individual weights for each sample.

Added in version 0.20: parameter sample_weight support to BayesianRidge.

Returns:

selfobject: Returns the instance itself.

get_metadata_routing()[source]#

get metadata routing of this object.

Please check User guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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, return_std=False)[source]#

Predict using the linear model.

In addition to the mean of the predictive distribution, also its standard deviation can be returned.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Samples.
return_stdbool, default=False: Whether to return the standard deviation of posterior prediction.

Returns:

y_meanarray-like of shape (n_samples,): Mean of predictive distribution of query points.
y_stdarray-like of shape (n_samples,): Standard deviation of predictive distribution of query points.

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 of y, 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), where n_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) w.r.t. y.

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANgED$') → BayesianRidge[source]#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANgED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED: Metadata routing for sample_weight parameter in fit.

Returns:

selfobject: The updated object.

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.

set_predict_request(*, return_std: bool | None | str = '$UNCHANgED$') → BayesianRidge[source]#

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANgED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

return_stdstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED: Metadata routing for return_std parameter in predict.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANgED$') → BayesianRidge[source]#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANgED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANgED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

gallery examples#

Feature agglomeration vs. univariate selection

Comparing Linear Bayesian Regressors

Curve Fitting with Bayesian Ridge Regression

L1-based models for Sparse Signals

Imputing missing values with variants of IterativeImputer