class_docstr: Base class for applying filtering operations on tabular data 
docstr: Method for making prediction for fit stage
  
  :param input_data: data with features, target and ids to process
  :return output_data: filtered input data by rows
docstr_tok: ["Method","for","making","prediction","for","fit","stage",":","param","input_data",":","data","with","features",",","target","and","ids","to","process",":","return","output_data",":","filtered","input","data","by","rows"]
code: def transform_for_fit(self, input_data: InputData) -> OutputData:
      """Method for making prediction for fit stage
  
      :param input_data: data with features, target and ids to process
      :return output_data: filtered input data by rows
      """
      # For fit stage - filter data
      mask = self.operation.inlier_mask_
      if mask is not None:
          input_data = update_data(input_data, mask)
      else:
          self.log.info(
              "Filtering Algorithm: didn't fit correctly. Return all objects"
          )
      output_data = self._convert_to_output(
          input_data, input_data.features
      )
      return output_data
  
imports: from copy import copy
  from typing import Optional
  import numpy
  import sklearn
  from golem.core.log import default_log
  from pkg_resources import parse_version
  from sklearn.ensemble import IsolationForest
  from sklearn.linear_model import LinearRegression, RANSACRegressor
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.tree import 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 4 for calling the FilterImplementation obj's underlying member method code with example usage: obj.transform_for_fit(input_data) and returns: output_data