type t =
[ `BaseEstimator
| `LassoLarsIC
| `MultiOutputMixin
| `Object
| `RegressorMixin ]
Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?criterion:[ `Bic | `Aic ] ->
?fit_intercept:bool ->
?verbose:int ->
?normalize:bool ->
?precompute:[ `Arr of [> `ArrayLike ] Np.Obj.t | `Auto | `Bool of bool ] ->
?max_iter:int ->
?eps:float ->
?copy_X:bool ->
?positive:bool ->
unit ->
tLasso model fit with Lars using BIC or AIC for model selection
The optimization objective for Lasso is::
(1 / (2 * n_samples)) * ||y - Xw||^2_2 + alpha * ||w||_1
AIC is the Akaike information criterion and BIC is the Bayes Information criterion. Such criteria are useful to select the value of the regularization parameter by making a trade-off between the goodness of fit and the complexity of the model. A good model should explain well the data while being simple.
Read more in the :ref:`User Guide <least_angle_regression>`.
Parameters ---------- criterion : 'bic' , 'aic', default='aic' The type of criterion to use.
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
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 can also be passed as argument.
max_iter : int, default=500 Maximum number of iterations to perform. Can be used for early stopping.
eps : float, optional The machine-precision regularization in the computation of the Cholesky diagonal factors. Increase this for very ill-conditioned systems. Unlike the ``tol`` parameter in some iterative optimization-based algorithms, this parameter does not control the tolerance of the optimization. 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 LassoLarsIC 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.
alpha_ : float the alpha parameter chosen by the information criterion
n_iter_ : int number of iterations run by lars_path to find the grid of alphas.
criterion_ : array-like of shape (n_alphas,) The value of the information criteria ('aic', 'bic') across all alphas. The alpha which has the smallest information criterion is chosen. This value is larger by a factor of ``n_samples`` compared to Eqns. 2.15 and 2.16 in (Zou et al, 2007).
Examples -------- >>> from sklearn import linear_model >>> reg = linear_model.LassoLarsIC(criterion='bic') >>> reg.fit([-1, 1], [0, 0], [1, 1], -1.1111, 0, -1.1111) LassoLarsIC(criterion='bic') >>> print(reg.coef_) 0. -1.11...
Notes ----- The estimation of the number of degrees of freedom is given by:
'On the degrees of freedom of the lasso' Hui Zou, Trevor Hastie, and Robert Tibshirani Ann. Statist. Volume 35, Number 5 (2007), 2173-2192.
https://en.wikipedia.org/wiki/Akaike_information_criterion https://en.wikipedia.org/wiki/Bayesian_information_criterion
See also -------- lars_path, LassoLars, LassoLarsCV
val fit :
?copy_X:bool ->
x:[> `ArrayLike ] Np.Obj.t ->
y:[> `ArrayLike ] Np.Obj.t ->
[> tag ] Obj.t ->
tFit 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. Will be cast to X's dtype if necessary
copy_X : bool, default=None If provided, this parameter will override the choice of copy_X made at instance creation. If ``True``, X will be copied; else, it may be overwritten.
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.
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 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.
Attribute criterion_: get value or raise Not_found if None.
Attribute criterion_: 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 : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.