code: def _find_scatter_plots_classification(
      X, target, how_many=3, random_state=None
  ):
      # input is continuous
      # look at all pairs of features, find most promising ones
      # converting to int here might save some time
      _, target = np.unique(target, return_inverse=True)
      scores = [
          (
              i,
              j,
              _score_triple(X, i, j, target, random_state=random_state),
          )
          for i, j in itertools.combinations(np.arange(X.shape[1]), 2)
      ]
      scores = pd.DataFrame(
          scores, columns=["feature0", "feature1", "score"]
      )
      top = scores.sort_values(by="score").iloc[-how_many:][::-1]
      return top
  
imports: from warnings import warn
  from functools import reduce
  import itertools
  import numpy
  import pandas
  import matplotlib.pyplot
  from matplotlib.axes import Axes
  from matplotlib.patches import Rectangle, Patch
  from matplotlib.ticker import EngFormatter
  from seaborn.utils import despine
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.metrics import roc_curve
  from sklearn.model_selection import cross_val_score, StratifiedShuffleSplit
  from ..preprocessing import detect_types
  from .._config import get_config
label_desc: Use image node_id 16 for calling a global function with example usage: _find_scatter_plots_classification(X, target, how_many, random_state) and returns: top