package sklearn

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type tag = [
  1. | `SelectPercentile
]
type t = [ `BaseEstimator | `Object | `SelectPercentile | `SelectorMixin | `TransformerMixin ] Obj.t
val of_pyobject : Py.Object.t -> t
val to_pyobject : [> tag ] Obj.t -> Py.Object.t
val as_transformer : t -> [ `TransformerMixin ] Obj.t
val as_estimator : t -> [ `BaseEstimator ] Obj.t
val as_selector : t -> [ `SelectorMixin ] Obj.t
val create : ?score_func:Py.Object.t -> ?percentile:int -> unit -> t

Select features according to a percentile of the highest scores.

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

Parameters ---------- score_func : callable Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or a single array with scores. Default is f_classif (see below 'See also'). The default function only works with classification tasks.

.. versionadded:: 0.18

percentile : int, optional, default=10 Percent of features to keep.

Attributes ---------- scores_ : array-like of shape (n_features,) Scores of features.

pvalues_ : array-like of shape (n_features,) p-values of feature scores, None if `score_func` returned only scores.

Examples -------- >>> from sklearn.datasets import load_digits >>> from sklearn.feature_selection import SelectPercentile, chi2 >>> X, y = load_digits(return_X_y=True) >>> X.shape (1797, 64) >>> X_new = SelectPercentile(chi2, percentile=10).fit_transform(X, y) >>> X_new.shape (1797, 7)

Notes ----- Ties between features with equal scores will be broken in an unspecified way.

See also -------- f_classif: ANOVA F-value between label/feature for classification tasks. mutual_info_classif: Mutual information for a discrete target. chi2: Chi-squared stats of non-negative features for classification tasks. f_regression: F-value between label/feature for regression tasks. mutual_info_regression: Mutual information for a continuous target. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. SelectFdr: Select features based on an estimated false discovery rate. SelectFwe: Select features based on family-wise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.

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

Run score function on (X, y) and get the appropriate features.

Parameters ---------- X : array-like of shape (n_samples, n_features) The training input samples.

y : array-like of shape (n_samples,) The target values (class labels in classification, real numbers in regression).

Returns ------- self : object

val fit_transform : ?y:[> `ArrayLike ] Np.Obj.t -> ?fit_params:(string * Py.Object.t) list -> x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters ---------- X : array-like, sparse matrix, dataframe of shape (n_samples, n_features)

y : ndarray of shape (n_samples,), default=None Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : ndarray array of shape (n_samples, n_features_new) Transformed array.

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 get_support : ?indices:bool -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Get a mask, or integer index, of the features selected

Parameters ---------- indices : boolean (default False) If True, the return value will be an array of integers, rather than a boolean mask.

Returns ------- support : array An index that selects the retained features from a feature vector. If `indices` is False, this is a boolean array of shape # input features, in which an element is True iff its corresponding feature is selected for retention. If `indices` is True, this is an integer array of shape # output features whose values are indices into the input feature vector.

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

Reverse the transformation operation

Parameters ---------- X : array of shape n_samples, n_selected_features The input samples.

Returns ------- X_r : array of shape n_samples, n_original_features `X` with columns of zeros inserted where features would have been removed by :meth:`transform`.

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 transform : x:[> `ArrayLike ] Np.Obj.t -> [> tag ] Obj.t -> [> `ArrayLike ] Np.Obj.t

Reduce X to the selected features.

Parameters ---------- X : array of shape n_samples, n_features The input samples.

Returns ------- X_r : array of shape n_samples, n_selected_features The input samples with only the selected features.

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

Attribute scores_: get value or raise Not_found if None.

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

Attribute scores_: get value as an option.

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

Attribute pvalues_: get value or raise Not_found if None.

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

Attribute pvalues_: 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 : Stdlib.Format.formatter -> t -> unit

Pretty-print the object to a formatter.