type t = [ `BaseEstimator | `BayesianRidge | `Object | `RegressorMixin ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?n_iter:int ->
?tol:float ->
?alpha_1:float ->
?alpha_2:float ->
?lambda_1:float ->
?lambda_2:float ->
?alpha_init:float ->
?lambda_init:float ->
?compute_score:bool ->
?fit_intercept:bool ->
?normalize:bool ->
?copy_X:bool ->
?verbose:int ->
unit ->
tBayesian 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 :ref:`User Guide <bayesian_regression>`.
Parameters ---------- n_iter : int, default=300 Maximum number of iterations. Should be greater than or equal to 1.
tol : float, default=1e-3 Stop the algorithm if w has converged.
alpha_1 : float, default=1e-6 Hyper-parameter : shape parameter for the Gamma distribution prior over the alpha parameter.
alpha_2 : float, default=1e-6 Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the alpha parameter.
lambda_1 : float, default=1e-6 Hyper-parameter : shape parameter for the Gamma distribution prior over the lambda parameter.
lambda_2 : float, default=1e-6 Hyper-parameter : inverse scale parameter (rate parameter) for the Gamma distribution prior over the lambda parameter.
alpha_init : float, default=None Initial value for alpha (precision of the noise). If not set, alpha_init is 1/Var(y).
.. versionadded:: 0.22
lambda_init : float, default=None Initial value for lambda (precision of the weights). If not set, lambda_init is 1.
.. versionadded:: 0.22
compute_score : bool, default=False If True, compute the log marginal likelihood at each iteration of the optimization.
fit_intercept : bool, 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).
normalize : bool, default=False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``.
copy_X : bool, default=True If True, X will be copied; else, it may be overwritten.
verbose : bool, 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.
Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.BayesianRidge() >>> clf.fit([0,0], [1, 1], [2, 2], 0, 1, 2) BayesianRidge() >>> clf.predict([1, 1]) array(1.)
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.
val fit :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tFit the model
Parameters ---------- X : ndarray of shape (n_samples, n_features) Training data y : ndarray of shape (n_samples,) Target values. Will be cast to X's dtype if necessary
sample_weight : ndarray of shape (n_samples,), default=None Individual weights for each sample
.. versionadded:: 0.20 parameter *sample_weight* support to BayesianRidge.
Returns ------- self : returns an instance of self.
Get parameters for this estimator.
Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns ------- params : mapping of string to any Parameter names mapped to their values.
val predict :
?return_std:bool ->
x:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
[> `ArrayLike ] Np.Obj.tPredict 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_std : bool, default=False Whether to return the standard deviation of posterior prediction.
Returns ------- y_mean : array-like of shape (n_samples,) Mean of predictive distribution of query points.
y_std : array-like of shape (n_samples,) Standard deviation of predictive distribution of query points.
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatReturn 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.
Parameters ---------- X : array-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, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float R^2 of self.predict(X) wrt. y.
Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).
val set_params : ?params:(string * Py.Object.t) list -> [> tag ] Obj.t -> tSet 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.
Parameters ---------- **params : dict Estimator parameters.
Returns ------- self : object Estimator instance.
Attribute intercept_: get value or raise Not_found if None.
Attribute intercept_: get value as an option.
val alpha_ : t -> floatAttribute alpha_: get value or raise Not_found if None.
val alpha_opt : t -> float optionAttribute alpha_: get value as an option.
val lambda_ : t -> floatAttribute lambda_: get value or raise Not_found if None.
val lambda_opt : t -> float optionAttribute lambda_: get value as an option.
val n_iter_ : t -> intAttribute n_iter_: get value or raise Not_found if None.
val n_iter_opt : t -> int optionAttribute n_iter_: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.