type t =
[ `BaseEstimator
| `ElasticNetCV
| `MultiOutputMixin
| `Object
| `RegressorMixin ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?l1_ratio:[ `Fs of float list | `F of float ] ->
?eps:float ->
?n_alphas:int ->
?alphas:[> `ArrayLike ] Np.Obj.t ->
?fit_intercept:bool ->
?normalize:bool ->
?precompute:[ `Arr of [> `ArrayLike ] Np.Obj.t | `Auto | `Bool of bool ] ->
?max_iter:int ->
?tol:float ->
?cv:
[ `BaseCrossValidator of [> `BaseCrossValidator ] Np.Obj.t
| `Arr of [> `ArrayLike ] Np.Obj.t
| `I of int ] ->
?copy_X:bool ->
?verbose:int ->
?n_jobs:int ->
?positive:bool ->
?random_state:int ->
?selection:[ `Cyclic | `Random ] ->
unit ->
tElastic Net model with iterative fitting along a regularization path.
See glossary entry for :term:`cross-validation estimator`.
Read more in the :ref:`User Guide <elastic_net>`.
Parameters ---------- l1_ratio : float or list of float, default=0.5 float between 0 and 1 passed to ElasticNet (scaling between l1 and l2 penalties). For ``l1_ratio = 0`` the penalty is an L2 penalty. For ``l1_ratio = 1`` it is an L1 penalty. For ``0 < l1_ratio < 1``, the penalty is a combination of L1 and L2 This parameter can be a list, in which case the different values are tested by cross-validation and the one giving the best prediction score is used. Note that a good choice of list of values for l1_ratio is often to put more values close to 1 (i.e. Lasso) and less close to 0 (i.e. Ridge), as in ``.1, .5, .7,
.9, .95, .99, 1``
eps : float, default=1e-3 Length of the path. ``eps=1e-3`` means that ``alpha_min / alpha_max = 1e-3``.
n_alphas : int, default=100 Number of alphas along the regularization path, used for each l1_ratio.
alphas : ndarray, default=None List of alphas where to compute the models. If None alphas are set automatically
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``.
precompute : 'auto', bool or array-like of shape (n_features, n_features), default='auto' Whether to use a precomputed Gram matrix to speed up calculations. If set to ``'auto'`` let us decide. The Gram matrix can also be passed as argument.
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``.
cv : int, cross-validation generator or iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are:
For int/None inputs, :class:`KFold` is used.
Refer :ref:`User Guide <cross_validation>` for the various cross-validation strategies that can be used here.
.. versionchanged:: 0.22 ``cv`` default value if None changed from 3-fold to 5-fold.
copy_X : bool, default=True If ``True``, X will be copied; else, it may be overwritten.
verbose : bool or int, default=0 Amount of verbosity.
n_jobs : int, default=None Number of CPUs to use during the cross validation. ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using all processors. See :term:`Glossary <n_jobs>` for more details.
positive : bool, default=False When set to ``True``, forces the coefficients to be positive.
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 ---------- alpha_ : float The amount of penalization chosen by cross validation
l1_ratio_ : float The compromise between l1 and l2 penalization chosen by cross validation
coef_ : ndarray of shape (n_features,) or (n_targets, n_features) Parameter vector (w in the cost function formula),
intercept_ : float or ndarray of shape (n_targets, n_features) Independent term in the decision function.
mse_path_ : ndarray of shape (n_l1_ratio, n_alpha, n_folds) Mean square error for the test set on each fold, varying l1_ratio and alpha.
alphas_ : ndarray of shape (n_alphas,) or (n_l1_ratio, n_alphas) The grid of alphas used for fitting, for each l1_ratio.
n_iter_ : int number of iterations run by the coordinate descent solver to reach the specified tolerance for the optimal alpha.
Examples -------- >>> from sklearn.linear_model import ElasticNetCV >>> from sklearn.datasets import make_regression
>>> X, y = make_regression(n_features=2, random_state=0) >>> regr = ElasticNetCV(cv=5, random_state=0) >>> regr.fit(X, y) ElasticNetCV(cv=5, random_state=0) >>> print(regr.alpha_) 0.199... >>> print(regr.intercept_) 0.398... >>> print(regr.predict([0, 0])) 0.398...
Notes ----- For an example, see :ref:`examples/linear_model/plot_lasso_model_selection.py <sphx_glr_auto_examples_linear_model_plot_lasso_model_selection.py>`.
To avoid unnecessary memory duplication the X argument of the fit method should be directly passed as a Fortran-contiguous numpy array.
The parameter l1_ratio corresponds to alpha in the glmnet R package while alpha corresponds to the lambda parameter in glmnet. More specifically, the optimization objective is::
1 / (2 * n_samples) * ||y - Xw||^2_2
If you are interested in controlling the L1 and L2 penalty separately, keep in mind that this is equivalent to::
a * L1 + b * L2
for::
alpha = a + b and l1_ratio = a / (a + b).
See also -------- enet_path ElasticNet
Fit linear model with coordinate descent
Fit is on grid of alphas and best alpha estimated by cross-validation.
Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training data. Pass directly as Fortran-contiguous data to avoid unnecessary memory duplication. If y is mono-output, X can be sparse.
y : array-like of shape (n_samples,) or (n_samples, n_targets) Target values
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.
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 l1_ratio_ : t -> floatAttribute l1_ratio_: get value or raise Not_found if None.
val l1_ratio_opt : t -> float optionAttribute l1_ratio_: get value as an option.
Attribute intercept_: get value or raise Not_found if None.
Attribute intercept_: get value as an option.
Attribute mse_path_: get value or raise Not_found if None.
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.