type t =
[ `BaseEstimator
| `LassoLarsCV
| `MultiOutputMixin
| `Object
| `RegressorMixin ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?fit_intercept:bool ->
?verbose:int ->
?max_iter:int ->
?normalize:bool ->
?precompute:[ `Auto | `Bool of bool ] ->
?cv:
[ `BaseCrossValidator of [> `BaseCrossValidator ] Np.Obj.t
| `Arr of [> `ArrayLike ] Np.Obj.t
| `I of int ] ->
?max_n_alphas:int ->
?n_jobs:int ->
?eps:float ->
?copy_X:bool ->
?positive:bool ->
unit ->
tCross-validated Lasso, using the LARS algorithm.
See glossary entry for :term:`cross-validation estimator`.
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters ---------- 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).
verbose : bool or int, default=False Sets the verbosity amount
max_iter : int, default=500 Maximum number of iterations to perform.
normalize : bool, default=True 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 : bool or 'auto' , default='auto' Whether to use a precomputed Gram matrix to speed up calculations. If set to ``'auto'`` let us decide. The Gram matrix cannot be passed as argument since we will use only subsets of X.
cv : int, cross-validation generator or an iterable, default=None Determines the cross-validation splitting strategy. Possible inputs for cv are:
For integer/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.
max_n_alphas : int, default=1000 The maximum number of points on the path used to compute the residuals in the cross-validation
n_jobs : int or None, 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.
eps : float, optional The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. By default, ``np.finfo(np.float).eps`` is used.
copy_X : bool, default=True If True, X will be copied; else, it may be overwritten.
positive : bool, default=False Restrict coefficients to be >= 0. Be aware that you might want to remove fit_intercept which is set True by default. Under the positive restriction the model coefficients do not converge to the ordinary-least-squares solution for small values of alpha. Only coefficients up to the smallest alpha value (``alphas_alphas_ >
0..min()`` when fit_path=True) reached by the stepwise Lars-Lasso algorithm are typically in congruence with the solution of the coordinate descent Lasso estimator. As a consequence using LassoLarsCV only makes sense for problems where a sparse solution is expected and/or reached.
Attributes ---------- coef_ : array-like of shape (n_features,) parameter vector (w in the formulation formula)
intercept_ : float independent term in decision function.
coef_path_ : array-like of shape (n_features, n_alphas) the varying values of the coefficients along the path
alpha_ : float the estimated regularization parameter alpha
alphas_ : array-like of shape (n_alphas,) the different values of alpha along the path
cv_alphas_ : array-like of shape (n_cv_alphas,) all the values of alpha along the path for the different folds
mse_path_ : array-like of shape (n_folds, n_cv_alphas) the mean square error on left-out for each fold along the path (alpha values given by ``cv_alphas``)
n_iter_ : array-like or int the number of iterations run by Lars with the optimal alpha.
Examples -------- >>> from sklearn.linear_model import LassoLarsCV >>> from sklearn.datasets import make_regression >>> X, y = make_regression(noise=4.0, random_state=0) >>> reg = LassoLarsCV(cv=5).fit(X, y) >>> reg.score(X, y) 0.9992... >>> reg.alpha_ 0.0484... >>> reg.predict(X:1,) array(-77.8723...)
Notes -----
The object solves the same problem as the LassoCV object. However, unlike the LassoCV, it find the relevant alphas values by itself. In general, because of this property, it will be more stable. However, it is more fragile to heavily multicollinear datasets.
It is more efficient than the LassoCV if only a small number of features are selected compared to the total number, for instance if there are very few samples compared to the number of features.
See also -------- lars_path, LassoLars, LarsCV, LassoCV
Fit the model using X, y as training data.
Parameters ---------- X : array-like of shape (n_samples, n_features) Training data.
y : array-like of shape (n_samples,) Target values.
Returns ------- self : object 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 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.
Attribute coef_path_: get value or raise Not_found if None.
Attribute coef_path_: 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.
Attribute cv_alphas_: get value or raise Not_found if None.
Attribute cv_alphas_: get value as an option.
Attribute mse_path_: get value or raise Not_found if None.
val n_iter_ : t -> Py.Object.tAttribute n_iter_: get value or raise Not_found if None.
val n_iter_opt : t -> Py.Object.t 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.