code: def get_feature_selection_model_from_name(
      type_of_estimator, model_name
  ):
      model_map = {
          "classifier": {
              "SelectFromModel": SelectFromModel(
                  RandomForestClassifier(
                      n_jobs=-1, max_depth=10, n_estimators=15
                  ),
                  threshold="20*mean",
              ),
              "RFECV": RFECV(
                  estimator=RandomForestClassifier(n_jobs=-1), step=0.1
              ),
              "GenericUnivariateSelect": GenericUnivariateSelect(),
              "KeepAll": "KeepAll",
          },
          "regressor": {
              "SelectFromModel": SelectFromModel(
                  RandomForestRegressor(
                      n_jobs=-1, max_depth=10, n_estimators=15
                  ),
                  threshold="0.7*mean",
              ),
              "RFECV": RFECV(
                  estimator=RandomForestRegressor(n_jobs=-1), step=0.1
              ),
              "GenericUnivariateSelect": GenericUnivariateSelect(),
              "KeepAll": "KeepAll",
          },
      }
  
      return model_map[type_of_estimator][model_name]
  
file_path: auto_ml/auto_ml/utils_feature_selection.py
imports: from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
  import scipy
  import itertools
  from sklearn.feature_selection import GenericUnivariateSelect, RFECV, SelectFromModel
label_desc: Use image node_id 1 for calling a global function with example usage: get_feature_selection_model_from_name(type_of_estimator, model_name) and returns: model_map