class_docstr: Return the Predictive Power Score of all features, in order to estimate each feature's ability to predict the label.
  
  The PPS represents the ability of a feature to single-handedly predict another feature or label.
  In this check, we specifically use it to assess the ability of each feature to predict the label.
  A high PPS (close to 1) can mean that this feature's success in predicting the label is actually due to data
  leakage - meaning that the feature holds information that is based on the label to begin with.
  
  When we compare train PPS to test PPS, A high difference can strongly indicate leakage,
  as a feature that was "powerful" in train but not in test can be explained by leakage in train that does
  not affect a new dataset.
  
  Uses the ppscore package - for more info, see https://github.com/8080labs/ppscore
  
  Parameters
  ----------
  ppscore_params : dict , default: None
      dictionary of additional parameters for the ppscore predictor function
  n_top_features : int , default: 5
      Number of features to show, sorted by the magnitude of difference in PPS
  n_samples : int , default: 100_000
      number of samples to use for this check.
  random_state : int , default: None
      Random state for the ppscore.predictors function
  min_pps_to_show: float, default 0.05
      Minimum PPS to show a class in the graph
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code: def __init__(
      self,
      ppscore_params=None,
      n_top_features: int = 5,
      n_samples: int = 100_000,
      random_state: int = None,
      min_pps_to_show: float = 0.05,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.ppscore_params = ppscore_params or {}
      self.n_top_features = n_top_features
      self.n_samples = n_samples
      self.random_state = random_state
      self.min_pps_to_show = min_pps_to_show
  
file_path: deepchecks/deepchecks/tabular/checks/train_test_validation/feature_label_correlation_change.py
imports: import typing
  from copy import copy
  import numpy
  from deepchecks.core import CheckResult, ConditionResult
  from deepchecks.core.check_utils.feature_label_correlation_utils import get_feature_label_correlation
  from deepchecks.core.condition import ConditionCategory
  from deepchecks.tabular import Context, TrainTestCheck
  from deepchecks.tabular.utils.messages import get_condition_passed_message
  from deepchecks.tabular.utils.task_type import TaskType
  from deepchecks.utils.strings import format_number
  from deepchecks.utils.typing import Hashable
label_desc: Use image node_id 1 to create a new FeatureLabelCorrelationChange object from inherited base classes: TrainTestCheck with example: obj = FeatureLabelCorrelationChange(ppscore_params, n_top_features, n_samples, random_state, min_pps_to_show)