class_docstr: Class for feature selection based on Recursive Feature Elimination (RFE) and
  LinearRegression as core model
  Task type - regression
class_docstr_tok: ["Class","for","feature","selection","based","on","Recursive","Feature","Elimination","(","RFE",")","and","LinearRegression","as","core","model","Task","type","-","regression"]
code: def __init__(self, params: Optional[OperationParameters]):
      super().__init__(params)
      self.inner_model = LinearRegression()
  
      if not self.params:
          # Default parameters
          self.operation = RFE(estimator=self.inner_model)
      else:
          # Checking the appropriate params are using or not
          rfe_params = {
              k: self.params.get(k)
              for k in ["n_features_to_select", "step"]
          }
          self.operation = RFE(estimator=self.inner_model, **rfe_params)
  
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 LinearRegFSImplementation object from inherited base classes: FeatureSelectionImplementation with example: obj = LinearRegFSImplementation(params)