package sklearn
Module Feature_selection.GenericUnivariateSelectSource
Source
val create :
?score_func:Py.Object.t ->
?mode:[ `Percentile | `K_best | `Fpr | `Fdr | `Fwe ] ->
?param:[ `Float of float | `PyObject of Py.Object.t ] ->
unit ->
tUnivariate feature selector with configurable strategy.
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). For modes 'percentile' or 'kbest' it can return a single array scores.
mode : 'percentile', 'k_best', 'fpr', 'fdr', 'fwe' Feature selection mode.
param : float or int depending on the feature selection mode Parameter of the corresponding mode.
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 scores only.
Examples -------- >>> from sklearn.datasets import load_breast_cancer >>> from sklearn.feature_selection import GenericUnivariateSelect, chi2 >>> X, y = load_breast_cancer(return_X_y=True) >>> X.shape (569, 30) >>> transformer = GenericUnivariateSelect(chi2, 'k_best', param=20) >>> X_new = transformer.fit_transform(X, y) >>> X_new.shape (569, 20)
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. 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. SelectFwe: Select features based on family-wise error rate.
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: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.
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.
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`.
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.
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.
Pretty-print the object to a formatter.