type t =
[ `BaseEstimator
| `MultiOutputMixin
| `MultiTaskLasso
| `Object
| `RegressorMixin ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?alpha:float ->
?fit_intercept:bool ->
?normalize:bool ->
?copy_X:bool ->
?max_iter:int ->
?tol:float ->
?warm_start:bool ->
?random_state:int ->
?selection:[ `Cyclic | `Random ] ->
unit ->
tMulti-task Lasso model trained with L1/L2 mixed-norm as regularizer.
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||Y - XW||^2_Fro + alpha * ||W||_21
Where::
||W||_21 = \sum_i \sqrt\sum_j w_{ij^2
}
i.e. the sum of norm of each row.
Read more in the :ref:`User Guide <multi_task_lasso>`.
Parameters ---------- alpha : float, default=1.0 Constant that multiplies the L1/L2 term. Defaults to 1.0
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.
max_iter : int, default=1000 The maximum number of iterations
tol : float, default=1e-4 The tolerance for the optimization: if the updates are smaller than ``tol``, the optimization code checks the dual gap for optimality and continues until it is smaller than ``tol``.
warm_start : bool, default=False When set to ``True``, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution. See :term:`the Glossary <warm_start>`.
random_state : int, RandomState instance, default=None The seed of the pseudo random number generator that selects a random feature to update. Used when ``selection`` == 'random'. Pass an int for reproducible output across multiple function calls. See :term:`Glossary <random_state>`.
selection : 'cyclic', 'random', default='cyclic' If set to 'random', a random coefficient is updated every iteration rather than looping over features sequentially by default. This (setting to 'random') often leads to significantly faster convergence especially when tol is higher than 1e-4
Attributes ---------- coef_ : ndarray of shape (n_tasks, n_features) Parameter vector (W in the cost function formula). Note that ``coef_`` stores the transpose of ``W``, ``W.T``.
intercept_ : ndarray of shape (n_tasks,) independent term in decision function.
n_iter_ : int number of iterations run by the coordinate descent solver to reach the specified tolerance.
Examples -------- >>> from sklearn import linear_model >>> clf = linear_model.MultiTaskLasso(alpha=0.1) >>> clf.fit([0, 1], [1, 2], [2, 4], [0, 0], [1, 1], [2, 3]) MultiTaskLasso(alpha=0.1) >>> print(clf.coef_) [0. 0.60809415]
[0. 0.94592424] >>> print(clf.intercept_) -0.41888636 -0.87382323
See also -------- MultiTaskLasso : Multi-task L1/L2 Lasso with built-in cross-validation Lasso MultiTaskElasticNet
Notes ----- The algorithm used to fit the model is coordinate descent.
To avoid unnecessary memory duplication the X and y arguments of the fit method should be directly passed as Fortran-contiguous numpy arrays.
Fit MultiTaskElasticNet model with coordinate descent
Parameters ---------- X : ndarray of shape (n_samples, n_features) Data y : ndarray of shape (n_samples, n_tasks) Target. Will be cast to X's dtype if necessary
Notes -----
Coordinate descent is an algorithm that considers each column of data at a time hence it will automatically convert the X input as a Fortran-contiguous numpy array if necessary.
To avoid memory re-allocation it is advised to allocate the initial data in memory directly using that format.
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 the linear model.
Parameters ---------- X : array_like or sparse matrix, shape (n_samples, n_features) Samples.
Returns ------- C : array, 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 ->
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 uses ``multioutput='uniform_average'`` from version 0.23 to keep consistent with default value of :func:`~sklearn.metrics.r2_score`. This influences the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`).
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.