code: def plot_classification_continuous(
      X,
      *,
      target_col,
      types=None,
      hue_order=None,
      scatter_alpha="auto",
      scatter_size="auto",
      univariate_plot="histogram",
      drop_outliers=True,
      plot_pairwise=True,
      top_k_interactions=10,
      random_state=None,
      **kwargs,
  ):
      """Plots for continuous features in classification.
  
      Selects important continuous features according to F statistics.
      Creates univariate distribution plots for these, as well as scatterplots
      for selected pairs of features, and scatterplots for selected pairs of
      PCA directions.
      If there are more than 2 classes, scatter plots from Linear Discriminant
      Analysis are also shown.
      Scatter plots are determined "interesting" is a decision tree on the
      two-dimensional projection performs well. The cross-validated macro-average
      recall of a decision tree is shown in the title for each scatterplot.
  
      Parameters
      ----------
      X : dataframe
          Input data including features and target.
      target_col : str or int
          Identifier of the target column in X.
      types : dataframe of types, optional.
          Output of detect_types on X. Can be used to avoid recomputing the
          types.
      scatter_alpha : float, default='auto'
          Alpha values for scatter plots. 'auto' is dirty hacks.
      scatter_size : float, default='auto'
          Marker size for scatter plots. 'auto' is dirty hacks.
      univariate_plot : string, default="histogram"
          Supported: 'histogram' and 'kde'.
      drop_outliers : bool, default=True
          Whether to drop outliers when plotting.
      plot_pairwise : bool, default=True
          Whether to create pairwise plots. Can be a bit slow.
      top_k_interactions : int, default=10
          How many pairwise interactions to consider
          (ranked by univariate f scores).
          Runtime is quadratic in this, but higher numbers might find more
          interesting interactions.
      random_state : int, None or numpy RandomState
          Random state used for subsampling for determining pairwise features
          to show.
  
      Notes
      -----
      important kwargs parameters are: scatter_size and scatter_alpha.
      """
  
      types = _check_X_target_col(
          X, target_col, types, task="classification"
      )
  
      features = X.loc[:, types.continuous + types.low_card_int_ordinal]
      if target_col in features.columns:
          features = features.drop(target_col, axis=1)
      if features.shape[1] == 0:
          return
  
      features_imp = SimpleImputer().fit_transform(features)
      target = X[target_col]
      figures = []
      if features.shape[1] <= 5:
          axes = pairplot(
              X,
              target_col=target_col,
              columns=features.columns,
              scatter_alpha=scatter_alpha,
              scatter_size=scatter_size,
          )
          title = "Continuous features"
          if features.shape[1] > 1:
              title = title + " pairplot"
          plt.suptitle(title, y=1.02)
  
          for ax in axes.ravel():
              _apply_eng_formatter(ax, which="x")
              _apply_eng_formatter(ax, which="y")
      else:
          # univariate plots
          f = _plot_univariate_classification(
              features,
              features_imp,
              target,
              drop_outliers,
              target_col,
              univariate_plot,
              hue_order,
          )
          figures.append(plt.gcf())
  
          # FIXME remove "variable = " from title, add f score
          # pairwise plots
          if not plot_pairwise:
              return figures
          top_k = np.argsort(f)[-top_k_interactions:][::-1]
          fig, axes = _plot_top_pairs(
              features_imp[:, top_k],
              target,
              types=types,
              scatter_alpha=scatter_alpha,
              scatter_size=scatter_size,
              feature_names=features.columns[top_k],
              how_many=4,
              random_state=random_state,
          )
          fig.suptitle("Top feature interactions")
      figures.append(axes)
      if not plot_pairwise:
          return figures
      # get some PCA directions
      # we're using all features here, not only most informative
      # should we use only those?
      n_components = min(
          top_k_interactions, features.shape[0], features.shape[1]
      )
      if n_components < 2:
          return figures
      features_scaled = _plot_pca_classification(
          n_components,
          features_imp,
          target,
          scatter_alpha=scatter_alpha,
          scatter_size=scatter_size,
          random_state=random_state,
      )
      figures.append(plt.gcf())
      # LDA
      fig = _plot_lda_classification(
          features_scaled,
          target,
          top_k_interactions,
          scatter_alpha=scatter_alpha,
          scatter_size=scatter_size,
          random_state=random_state,
      )
      figures.append(fig)
      return figures
  
imports: import warnings
  import numpy
  import matplotlib.pyplot
  import matplotlib
  import seaborn
  import pandas
  from sklearn.feature_selection import mutual_info_regression, mutual_info_classif, f_classif
  from sklearn.impute import SimpleImputer
  from sklearn.preprocessing import scale
  from sklearn.decomposition import PCA
  from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
  from sklearn.metrics import recall_score
  from ..preprocessing import detect_types, clean
  from .utils import _check_X_target_col, _get_n_top, _make_subplots, _short_tick_names, _shortname, _prune_category_make_X, find_pretty_grid, _find_scatter_plots_classification, class_hists, discrete_scatter, mosaic_plot, _find_inliers, pairplot, _get_scatter_alpha, _get_scatter_size, _find_categorical_for_regression, _prune_categories, _apply_eng_formatter
  from .sankey import plot_sankey
  from warnings import warn
label_desc: Use image node_id 3 for calling a global function with example usage: plot_classification_continuous(X) and returns: figures, figures, figures, figures