class_docstr: Isolation Forest algorithm based on ExtraTreeRegressor
  Task type - classification
docstr: Method for checking if inlier mask is correct
  Inlier mask considered correct if after its application no class is completely or partially
  removed from the dataset by more than 80 percent
  
  :param input_targets: targets from input_data
  :param modified_targets: targets from modified_targets
  :return True if mask is correct otherwise False
docstr_tok: ["Method","for","checking","if","inlier","mask","is","correct","Inlier","mask","considered","correct","if","after","its","application","no","class","is","completely","or","partially","removed","from","the","dataset","by","more","than","80","percent",":","param","input_targets",":","targets","from","input_data",":","param","modified_targets",":","targets","from","modified_targets",":","return","True","if","mask","is","correct","otherwise","False"]
code: def _is_inlier_mask_correct(
      input_targets: np.ndarray, modified_targets: np.ndarray
  ) -> bool:
      """Method for checking if inlier mask is correct
      Inlier mask considered correct if after its application no class is completely or partially
      removed from the dataset by more than 80 percent
  
      :param input_targets: targets from input_data
      :param modified_targets: targets from modified_targets
      :return True if mask is correct otherwise False
      """
  
      if (
          np.unique(input_targets).shape
          != np.unique(modified_targets).shape
      ):
          return False
      for counts in zip(
          np.unique(input_targets, return_counts=True)[1],
          np.unique(modified_targets, return_counts=True)[1],
      ):
          if counts[0] > 5 * counts[1]:
              return False
      return True
  
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 1 for calling the IsolationForestClassImplementation obj's underlying member method code with example usage: obj._is_inlier_mask_correct(input_targets, modified_targets) and returns: True, False, False