package sklearn

You can search for identifiers within the package.

in-package search v0.2.0

sklearn
- Sklearn

Legend:
Library
Module
Module type
Parameter
Class
Class type

type tag = [

| `OrthogonalMatchingPursuitCV

]

type t =
  [ `BaseEstimator | `Object | `OrthogonalMatchingPursuitCV | `RegressorMixin ]
    Obj.t

val of_pyobject : Py.Object.t -> t

val to_pyobject : [> tag ] Obj.t -> Py.Object.t

val as_estimator : t -> [ `BaseEstimator ] Obj.t

val as_regressor : t -> [ `RegressorMixin ] Obj.t

val create : 
  ?copy:bool ->
  ?fit_intercept:bool ->
  ?normalize:bool ->
  ?max_iter:int ->
  ?cv:
    [ `BaseCrossValidator of [> `BaseCrossValidator ] Np.Obj.t
    | `Arr of [> `ArrayLike ] Np.Obj.t
    | `I of int ] ->
  ?n_jobs:int ->
  ?verbose:int ->
  unit ->
  t

Cross-validated Orthogonal Matching Pursuit model (OMP).

See glossary entry for :term:`cross-validation estimator`.

Read more in the :ref:`User Guide <omp>`.

Parameters ---------- copy : bool, optional Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway.

fit_intercept : boolean, optional 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 : boolean, optional, 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``.

max_iter : integer, optional Maximum numbers of iterations to perform, therefore maximum features to include. 10% of ``n_features`` but at least 5 if available.

cv : int, cross-validation generator or an iterable, optional Determines the cross-validation splitting strategy. Possible inputs for cv are:

None, to use the default 5-fold cross-validation,
integer, to specify the number of folds.
:term:`CV splitter`,
An iterable yielding (train, test) splits as arrays of indices.

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.

n_jobs : int or None, optional (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.

verbose : boolean or integer, optional Sets the verbosity amount

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

coef_ : array, shape (n_features,) or (n_targets, n_features) Parameter vector (w in the problem formulation).

n_nonzero_coefs_ : int Estimated number of non-zero coefficients giving the best mean squared error over the cross-validation folds.

n_iter_ : int or array-like Number of active features across every target for the model refit with the best hyperparameters got by cross-validating across all folds.

Examples -------- >>> from sklearn.linear_model import OrthogonalMatchingPursuitCV >>> from sklearn.datasets import make_regression >>> X, y = make_regression(n_features=100, n_informative=10, ... noise=4, random_state=0) >>> reg = OrthogonalMatchingPursuitCV(cv=5).fit(X, y) >>> reg.score(X, y) 0.9991... >>> reg.n_nonzero_coefs_ 10 >>> reg.predict(X:1,) array(-78.3854...)

See also -------- orthogonal_mp orthogonal_mp_gram lars_path Lars LassoLars OrthogonalMatchingPursuit LarsCV LassoLarsCV decomposition.sparse_encode

val fit : 
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  t

Fit the model using X, y as training data.

Parameters ---------- X : array-like, shape n_samples, n_features Training data.

y : array-like, shape n_samples Target values. Will be cast to X's dtype if necessary

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

val get_params : ?deep:bool -> [> tag ] Obj.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:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.t ->
  [> `ArrayLike ] Np.Obj.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:[> `ArrayLike ] Np.Obj.t ->
  x:[> `ArrayLike ] Np.Obj.t ->
  y:[> `ArrayLike ] Np.Obj.t ->
  [> tag ] Obj.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 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 -> 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 intercept_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute intercept_: get value or raise Not_found if None.

val intercept_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute intercept_: get value as an option.

val coef_ : t -> [> `ArrayLike ] Np.Obj.t

Attribute coef_: get value or raise Not_found if None.

val coef_opt : t -> [> `ArrayLike ] Np.Obj.t option

Attribute coef_: get value as an option.

val n_nonzero_coefs_ : t -> int

Attribute n_nonzero_coefs_: get value or raise Not_found if None.

val n_nonzero_coefs_opt : t -> int option

Attribute n_nonzero_coefs_: get value as an option.

val n_iter_ : t -> Py.Object.t

Attribute n_iter_: get value or raise Not_found if None.

val n_iter_opt : t -> Py.Object.t 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.