package sklearn

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sklearn
- Sklearn

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type tag = [

| `SelectFpr

]

type t =
  [ `BaseEstimator
  | `Object
  | `SelectFpr
  | `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_selector : t -> [ `SelectorMixin ] Obj.t

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

val create : ?score_func:Py.Object.t -> ?alpha:float -> unit -> t

Filter: Select the pvalues below alpha based on a FPR test.

FPR test stands for False Positive Rate test. It controls the total amount of false detections.

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). Default is f_classif (see below 'See also'). The default function only works with classification tasks.

alpha : float, optional The highest p-value for features to be kept.

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

pvalues_ : array-like of shape (n_features,) p-values of feature scores.

Examples -------- >>> from sklearn.datasets import load_breast_cancer >>> from sklearn.feature_selection import SelectFpr, chi2 >>> X, y = load_breast_cancer(return_X_y=True) >>> X.shape (569, 30) >>> X_new = SelectFpr(chi2, alpha=0.01).fit_transform(X, y) >>> X_new.shape (569, 16)

See also -------- f_classif: ANOVA F-value between label/feature for classification tasks. chi2: Chi-squared stats of non-negative features for classification tasks. mutual_info_classif: f_regression: F-value between label/feature for regression tasks. mutual_info_regression: Mutual information between features and the target. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. 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 : numpy array of shape n_samples, n_features Training set.

y : numpy array of shape n_samples Target values.

**fit_params : dict Additional fit parameters.

Returns ------- X_new : numpy 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 : Format.formatter -> t -> unit

Pretty-print the object to a formatter.