type t = [ `ARDRegression | `BaseEstimator | `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 ->
?compute_score:bool ->
?threshold_lambda:float ->
?fit_intercept:bool ->
?normalize:bool ->
?copy_X:bool ->
?verbose:int ->
unit ->
tBayesian ARD regression.
Fit the weights of a regression model, using an ARD prior. The weights of the regression model are assumed to be in Gaussian distributions. Also estimate the parameters lambda (precisions of the distributions of the weights) and alpha (precision of the distribution of the noise). The estimation is done by an iterative procedures (Evidence Maximization)
Read more in the :ref:`User Guide <bayesian_regression>`.
Parameters ---------- n_iter : int, default=300 Maximum number of iterations.
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.
compute_score : bool, default=False If True, compute the objective function at each step of the model.
threshold_lambda : float, default=10 000 threshold for removing (pruning) weights with high precision from the computation.
fit_intercept : bool, default=True whether to calculate the intercept for this model. 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)
alpha_ : float estimated precision of the noise.
lambda_ : array-like of shape (n_features,) estimated precisions of the weights.
sigma_ : array-like of shape (n_features, n_features) estimated variance-covariance matrix of the weights
scores_ : float if computed, value of the objective function (to be maximized)
intercept_ : float Independent term in decision function. Set to 0.0 if ``fit_intercept = False``.
Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.ARDRegression() >>> clf.fit([0,0], [1, 1], [2, 2], 0, 1, 2) ARDRegression() >>> clf.predict([1, 1]) array(1.)
Notes ----- For an example, see :ref:`examples/linear_model/plot_ard.py <sphx_glr_auto_examples_linear_model_plot_ard.py>`.
References ---------- D. J. C. MacKay, Bayesian nonlinear modeling for the prediction competition, ASHRAE Transactions, 1994.
R. Salakhutdinov, Lecture notes on Statistical Machine Learning, http://www.utstat.toronto.edu/~rsalakhu/sta4273/notes/Lecture2.pdf#page=15 Their beta is our ``self.alpha_`` Their alpha is our ``self.lambda_`` ARD is a little different than the slide: only dimensions/features for which ``self.lambda_ < self.threshold_lambda`` are kept and the rest are discarded.
Fit the ARDRegression model according to the given training data and parameters.
Iterative procedure to maximize the evidence
Parameters ---------- X : array-like of shape (n_samples, n_features) Training vector, where n_samples in the number of samples and n_features is the number of features. y : array-like of shape (n_samples,) Target values (integers). Will be cast to X's dtype if necessary
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.
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 scores_ : t -> floatAttribute scores_: get value or raise Not_found if None.
val scores_opt : t -> float optionAttribute scores_: get value as an option.
Attribute intercept_: get value or raise Not_found if None.
Attribute intercept_: 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.