Module `Feature_selection.SelectFdr`Source

Sourcetype t

Sourceval of_pyobject : Py.Object.t -> t

Sourceval to_pyobject : t -> Py.Object.t

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

Filter: Select the p-values for an estimated false discovery rate

This uses the Benjamini-Hochberg procedure. ``alpha`` is an upper bound on the expected false discovery rate.

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 uncorrected p-value for features to keep.

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

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

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

References ---------- https://en.wikipedia.org/wiki/False_discovery_rate

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 contnuous target. SelectPercentile: Select features based on percentile of the highest scores. SelectKBest: Select features based on the k highest scores. SelectFpr: Select features based on a false positive rate test. SelectFwe: Select features based on family-wise error rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.

Sourceval fit : x:Arr.t -> y:Arr.t -> 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

Source

val fit_transform : 
  ?y:Arr.t ->
  ?fit_params:(string * Py.Object.t) list ->
  x:Arr.t ->
  t ->
  Arr.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.

Sourceval 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.

Sourceval get_support : ?indices:bool -> t -> Arr.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.

Sourceval inverse_transform : x:Arr.t -> t -> Arr.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`.

Sourceval 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.

Sourceval transform : x:Arr.t -> t -> Arr.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.

Sourceval scores_ : t -> Arr.t

Attribute scores_: get value or raise Not_found if None.

Sourceval scores_opt : t -> Arr.t option

Attribute scores_: get value as an option.

Sourceval pvalues_ : t -> Arr.t

Attribute pvalues_: get value or raise Not_found if None.

Sourceval pvalues_opt : t -> Arr.t option

Attribute pvalues_: get value as an option.

Sourceval to_string : t -> string

Print the object to a human-readable representation.

Sourceval show : t -> string

Print the object to a human-readable representation.

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

Pretty-print the object to a formatter.

package sklearn

Module Feature_selection.SelectFdrSource

Module `Feature_selection.SelectFdr`Source