val of_pyobject : Py.Object.t -> tval to_pyobject : t -> Py.Object.tval create : ?score_func:Py.Object.t -> ?alpha:float -> unit -> tFilter: Select the p-values corresponding to Family-wise error 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 SelectFwe, chi2 >>> X, y = load_breast_cancer(return_X_y=True) >>> X.shape (569, 30) >>> X_new = SelectFwe(chi2, alpha=0.01).fit_transform(X, y) >>> X_new.shape (569, 15)
Attributes ---------- scores_ : array-like of shape (n_features,) Scores of features.
pvalues_ : array-like of shape (n_features,) p-values of feature scores.
See also -------- f_classif: ANOVA F-value between label/feature for classification tasks. chi2: Chi-squared stats of non-negative features for classification tasks. f_regression: F-value between label/feature for regression tasks. 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. SelectFdr: Select features based on an estimated false discovery rate. GenericUnivariateSelect: Univariate feature selector with configurable mode.
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:Ndarray.t ->
?fit_params:(string * Py.Object.t) list ->
x:Ndarray.t ->
t ->
Ndarray.tFit 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 -> t -> Py.Object.tGet 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.
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.
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 -> t -> tSet 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.
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 to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Format.formatter -> t -> unitPretty-print the object to a formatter.