class_docstr: This masks out tabular features by integrating over the given background dataset.
      
class_docstr_tok: ["This","masks","out","tabular","features","by","integrating","over","the","given","background","dataset","."]
docstr: Build a Independent masker with the given background data.
  
  Parameters
  ----------
  data : numpy.ndarray, pandas.DataFrame
      The background dataset that is used for masking.
  
  max_samples : int
      The maximum number of samples to use from the passed background data. If data has more
      than max_samples then shap.utils.sample is used to subsample the dataset. The number of
      samples coming out of the masker (to be integrated over) matches the number of samples in
      the background dataset. This means larger background dataset cause longer runtimes. Normally
      about 1, 10, 100, or 1000 background samples are reasonable choices.
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code: def __init__(self, data, max_samples=100):
      """Build a Independent masker with the given background data.
  
      Parameters
      ----------
      data : numpy.ndarray, pandas.DataFrame
          The background dataset that is used for masking.
  
      max_samples : int
          The maximum number of samples to use from the passed background data. If data has more
          than max_samples then shap.utils.sample is used to subsample the dataset. The number of
          samples coming out of the masker (to be integrated over) matches the number of samples in
          the background dataset. This means larger background dataset cause longer runtimes. Normally
          about 1, 10, 100, or 1000 background samples are reasonable choices.
      """
      super().__init__(data, max_samples=max_samples, clustering=None)
  
imports: import logging
  import numpy
  import pandas
  from numba import njit
  from ..None import utils
  from .._serializable import Deserializer, Serializer
  from ..utils import MaskedModel
  from ..utils._exceptions import DimensionError, InvalidClusteringError
  from ._masker import Masker
label_desc: Use image node_id 1 to create a new Independent object from inherited base classes: Tabular with example: obj = Independent(data, max_samples)