type t =
[ `BaseEstimator | `LarsCV | `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:[ `Arr of [> `ArrayLike ] Np.Obj.t | `Auto | `Bool of bool ] ->
?cv:
[ `Arr of [> `ArrayLike ] Np.Obj.t
| `BaseCrossValidator of [> `BaseCrossValidator ] Np.Obj.t
| `I of int ] ->
?max_n_alphas:int ->
?n_jobs:int ->
?eps:float ->
?copy_X:bool ->
unit ->
tCross-validated Least Angle Regression model.
See glossary entry for :term:`cross-validation estimator`.
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, 'auto' or array-like , 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.
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 LarsCV >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_samples=200, noise=4.0, random_state=0) >>> reg = LarsCV(cv=5).fit(X, y) >>> reg.score(X, y) 0.9996... >>> reg.alpha_ 0.0254... >>> reg.predict(X:1,) array(154.0842...)
See also -------- lars_path, LassoLars, LassoLarsCV
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 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.
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.