class_docstr: Class for applying feature selection operations on tabular data 
class_docstr_tok: ["Class","for","applying","feature","selection","operations","on","tabular","data"]
code: def __init__(self, params: Optional[OperationParameters]):
      super().__init__(params)
      self.inner_model = None
      self.operation = None
      self.is_not_fitted = None
  
      # Number of columns in features table
      self.features_columns_number = None
  
      # Bool mask where True - remain column and False - drop it
      self.remain_features_mask = None
  
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 1 to create a new FeatureSelectionImplementation object from inherited base classes: DataOperationImplementation with example: obj = FeatureSelectionImplementation(params)