class_docstr: Univariate 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, shape=(n_features,)
      Scores of features.
  
  pvalues_ : array-like, shape=(n_features,)
      p-values of feature scores, None if `score_func` returned scores only.
  
  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.
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code: def __init__(
      self, score_func=f_classif, mode="percentile", param=1e-5
  ):
      super(GenericUnivariateSelect, self).__init__(score_func)
      self.mode = mode
      self.param = param
  
imports: import numpy
  import warnings
  from scipy import special, stats
  from scipy.sparse import issparse
  from ..base import BaseEstimator
  from ..preprocessing import LabelBinarizer
  from ..utils import as_float_array, check_array, check_X_y, safe_sqr, safe_mask
  from ..utils.extmath import norm, safe_sparse_dot, row_norms
  from ..utils.validation import check_is_fitted
  from .base import SelectorMixin
label_desc: Use image node_id 1 to create a new GenericUnivariateSelect object from inherited base classes: _BaseFilter with example: obj = GenericUnivariateSelect(score_func, mode, param)