class_docstr: Provides a marginal plot for provided data.
docstr: Initializes class.
  
  Args:
      feature_names: List of feature names.
      feature_types: List of feature types.
      max_scatter_samples: Number of sample points in visualization.
docstr_tok: ["Initializes","class",".","Args",":","feature_names",":","List","of","feature","names",".","feature_types",":","List","of","feature","types",".","max_scatter_samples",":","Number","of","sample","points","in","visualization","."]
code: def __init__(
      self,
      feature_names=None,
      feature_types=None,
      max_scatter_samples=400,
  ):
      """Initializes class.
  
      Args:
          feature_names: List of feature names.
          feature_types: List of feature types.
          max_scatter_samples: Number of sample points in visualization.
      """
      self.feature_names = feature_names
      self.feature_types = feature_types
      self.max_scatter_samples = max_scatter_samples
  
imports: from ..api.base import ExplainerMixin, ExplanationMixin
  from ..utils._explanation import gen_name_from_class, gen_global_selector
  import numpy
  from scipy.stats import pearsonr
  from ..utils._clean_x import preclean_X
  from ..utils._clean_simple import clean_dimensions, typify_classification
  from ..utils._unify_data import unify_data
label_desc: Use image node_id 1 to create a new Marginal object from inherited base classes: ExplainerMixin with example: obj = Marginal(feature_names, feature_types, max_scatter_samples)