class_docstr: Checks for pairwise correlation between the features.
  
  Extremely correlated pairs of features could indicate redundancy and even duplication.
  Removing highly correlated features from the data can significantly increase model speed due to the curse of
  dimensionality, and decrease harmful bias.
  
  Parameters
  ----------
  columns : Union[Hashable, List[Hashable]] , default: None
      Columns to check, if none are given checks all columns except ignored ones.
  ignore_columns : Union[Hashable, List[Hashable]] , default: None
      Columns to ignore, if none given checks based on columns variable.
  show_n_top_columns : int , optional
      amount of columns to show ordered by the highest correlation, default: 10
  n_samples : int , default: 10_000
      number of samples to use for this check.
  random_state : int, default: 42
      random seed for all check internals.
code: def __init__(
      self,
      columns: Union[Hashable, List[Hashable], None] = None,
      ignore_columns: Union[Hashable, List[Hashable], None] = None,
      show_n_top_columns: int = 10,
      n_samples: int = 10_000,
      random_state: int = 42,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.columns = columns
      self.ignore_columns = ignore_columns
      self.n_top_columns = show_n_top_columns
      self.n_samples = n_samples
      self.random_state = random_state
  
file_path: deepchecks/deepchecks/tabular/checks/data_integrity/feature_feature_correlation.py
imports: from typing import List, Union
  import numpy
  import pandas
  import plotly.express
  from deepchecks.core import CheckResult, ConditionCategory, ConditionResult
  from deepchecks.tabular import Context, SingleDatasetCheck
  from deepchecks.utils.correlation_methods import correlation_ratio, symmetric_theil_u_correlation
  from deepchecks.utils.dataframes import generalized_corrwith, select_from_dataframe
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new FeatureFeatureCorrelation object from inherited base classes: SingleDatasetCheck with example: obj = FeatureFeatureCorrelation(columns, ignore_columns, show_n_top_columns, n_samples, random_state)