package sklearn

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sklearn
- Sklearn

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Class type

type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?fit_intercept:bool ->
  ?verbose:int ->
  ?normalize:bool ->
  ?precompute:[ `Bool of bool | `Auto | `Arr of Arr.t ] ->
  ?n_nonzero_coefs:int ->
  ?eps:float ->
  ?copy_X:bool ->
  ?fit_path:bool ->
  unit ->
  t

Least Angle Regression model a.k.a. LAR

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

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.

n_nonzero_coefs : int, default=500 Target number of non-zero coefficients. Use ``np.inf`` for no limit.

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.

fit_path : bool, default=True If True the full path is stored in the ``coef_path_`` attribute. If you compute the solution for a large problem or many targets, setting ``fit_path`` to ``False`` will lead to a speedup, especially with a small alpha.

Attributes ---------- alphas_ : array-like of shape (n_alphas + 1,) | list of n_targets such arrays Maximum of covariances (in absolute value) at each iteration. ``n_alphas`` is either ``n_nonzero_coefs`` or ``n_features``, whichever is smaller.

active_ : list, length = n_alphas | list of n_targets such lists Indices of active variables at the end of the path.

coef_path_ : array-like of shape (n_features, n_alphas + 1) | list of n_targets such arrays The varying values of the coefficients along the path. It is not present if the ``fit_path`` parameter is ``False``.

coef_ : array-like of shape (n_features,) or (n_targets, n_features) Parameter vector (w in the formulation formula).

intercept_ : float or array-like of shape (n_targets,) Independent term in decision function.

n_iter_ : array-like or int The number of iterations taken by lars_path to find the grid of alphas for each target.

Examples -------- >>> from sklearn import linear_model >>> reg = linear_model.Lars(n_nonzero_coefs=1) >>> reg.fit([-1, 1], [0, 0], [1, 1], -1.1111, 0, -1.1111) Lars(n_nonzero_coefs=1) >>> print(reg.coef_) 0. -1.11...

See also -------- lars_path, LarsCV sklearn.decomposition.sparse_encode

val fit : ?xy:Arr.t -> x:Arr.t -> y:Arr.t -> t -> t

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,) or (n_samples, n_targets) Target values.

Xy : array-like of shape (n_samples,) or (n_samples, n_targets), default=None Xy = np.dot(X.T, y) that can be precomputed. It is useful only when the Gram matrix is precomputed.

Returns ------- self : object returns an instance of self.

val get_params : ?deep:bool -> t -> Dict.t

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.

val predict : x:Arr.t -> t -> Arr.t

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:Arr.t -> x:Arr.t -> y:Arr.t -> t -> float

Return 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 -> t -> t

Set 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 alphas_ : t -> Py.Object.t

Attribute alphas_: get value or raise Not_found if None.

val alphas_opt : t -> Py.Object.t option

Attribute alphas_: get value as an option.

val active_ : t -> Py.Object.t

Attribute active_: get value or raise Not_found if None.

val active_opt : t -> Py.Object.t option

Attribute active_: get value as an option.

val coef_path_ : t -> Py.Object.t

Attribute coef_path_: get value or raise Not_found if None.

val coef_path_opt : t -> Py.Object.t option

Attribute coef_path_: get value as an option.

val coef_ : t -> Arr.t

Attribute coef_: get value or raise Not_found if None.

val coef_opt : t -> Arr.t option

Attribute coef_: get value as an option.

val intercept_ : t -> Arr.t

Attribute intercept_: get value or raise Not_found if None.

val intercept_opt : t -> Arr.t option

Attribute intercept_: get value as an option.

val n_iter_ : t -> [ `Arr of Arr.t | `I of int ]

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> [ `Arr of Arr.t | `I of int ] option

Attribute n_iter_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.