type t =
[ `BaseEstimator | `Object | `RegressorMixin | `TweedieRegressor ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?power:float ->
?alpha:float ->
?fit_intercept:bool ->
?link:[ `Auto | `Identity | `Log ] ->
?max_iter:int ->
?tol:float ->
?warm_start:bool ->
?verbose:int ->
unit ->
tGeneralized Linear Model with a Tweedie distribution.
This estimator can be used to model different GLMs depending on the ``power`` parameter, which determines the underlying distribution.
Read more in the :ref:`User Guide <Generalized_linear_regression>`.
Parameters ---------- power : float, default=0 The power determines the underlying target distribution according to the following table:
+-------+------------------------+ | Power | Distribution | +=======+========================+ | 0 | Normal | +-------+------------------------+ | 1 | Poisson | +-------+------------------------+ | (1,2) | Compound Poisson Gamma | +-------+------------------------+ | 2 | Gamma | +-------+------------------------+ | 3 | Inverse Gaussian | +-------+------------------------+
For ``0 < power < 1``, no distribution exists.
alpha : float, default=1 Constant that multiplies the penalty term and thus determines the regularization strength. ``alpha = 0`` is equivalent to unpenalized GLMs. In this case, the design matrix `X` must have full column rank (no collinearities).
link : 'auto', 'identity', 'log', default='auto' The link function of the GLM, i.e. mapping from linear predictor `X @ coeff + intercept` to prediction `y_pred`. Option 'auto' sets the link depending on the chosen family as follows:
fit_intercept : bool, default=True Specifies if a constant (a.k.a. bias or intercept) should be added to the linear predictor (X @ coef + intercept).
max_iter : int, default=100 The maximal number of iterations for the solver.
tol : float, default=1e-4 Stopping criterion. For the lbfgs solver, the iteration will stop when ``max |g_j|, j = 1, ..., d <= tol`` where ``g_j`` is the j-th component of the gradient (derivative) of the objective function.
warm_start : bool, default=False If set to ``True``, reuse the solution of the previous call to ``fit`` as initialization for ``coef_`` and ``intercept_`` .
verbose : int, default=0 For the lbfgs solver set verbose to any positive number for verbosity.
Attributes ---------- coef_ : array of shape (n_features,) Estimated coefficients for the linear predictor (`X @ coef_ + intercept_`) in the GLM.
intercept_ : float Intercept (a.k.a. bias) added to linear predictor.
n_iter_ : int Actual number of iterations used in the solver.
val fit :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tFit a Generalized Linear Model.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training data.
y : array-like of shape (n_samples,) Target values.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
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.
Predict using GLM with feature matrix X.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Samples.
Returns ------- y_pred : array of shape (n_samples,) Returns predicted values.
val score :
?sample_weight:[> `ArrayLike ] Np.Obj.t ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
floatCompute D^2, the percentage of deviance explained.
D^2 is a generalization of the coefficient of determination R^2. R^2 uses squared error and D^2 deviance. Note that those two are equal for ``family='normal'``.
D^2 is defined as :math:`D^2 = 1-\fracD(y_{true,y_pred)
}
D_{null
}
`, :math:`D_null` is the null deviance, i.e. the deviance of a model with intercept alone, which corresponds to :math:`y_pred = \bary`. The mean :math:`\bary` is averaged by sample_weight. Best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse).
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Test samples.
y : array-like of shape (n_samples,) True values of target.
sample_weight : array-like of shape (n_samples,), default=None Sample weights.
Returns ------- score : float D^2 of self.predict(X) w.r.t. y.
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 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.