class_docstr: Class for applying feature selection operations on tabular data 
class_docstr_tok: ["Class","for","applying","feature","selection","operations","on","tabular","data"]
docstr: The method creates a table based on transformed data and source boolean
  features
  
  :param features: tabular data for processing
  :return transformed_features: transformed features table
docstr_tok: ["The","method","creates","a","table","based","on","transformed","data","and","source","boolean","features",":","param","features",":","tabular","data","for","processing",":","return","transformed_features",":","transformed","features","table"]
code: def _make_new_table(self, features):
      """
      The method creates a table based on transformed data and source boolean
      features
  
      :param features: tabular data for processing
      :return transformed_features: transformed features table
      """
  
      # Bool vector - mask for columns
      self.remain_features_mask = self.operation.support_
      transformed_features = features[:, self.remain_features_mask]
      return transformed_features
  
imports: from typing import Optional
  import numpy
  from sklearn.feature_selection import RFE
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from fedot.core.data.data import InputData, OutputData
  from fedot.core.operations.evaluation.operation_implementations.implementation_interfaces import DataOperationImplementation
  from fedot.core.operations.operation_parameters import OperationParameters
label_desc: Use image node_id 5 for calling the FeatureSelectionImplementation obj's underlying member method code with example usage: obj._make_new_table(features) and returns: transformed_features