code: def oob_dropcol_importances(rf, X_train, y_train):
      """
      Compute drop-column feature importances for scikit-learn.
  
      Given a RandomForestClassifier or RandomForestRegressor in rf
      and training X and y data, return a data frame with columns
      Feature and Importance sorted in reverse order by importance.
  
      A clone of rf is trained once to get the baseline score and then
      again, once per feature to compute the drop in out of bag (OOB)
      score.
  
      return: A data frame with Feature, Importance columns
  
      SAMPLE CODE
  
      rf = RandomForestRegressor(n_estimators=100, n_jobs=-1, oob_score=True)
      X_train, y_train = ..., ...
      rf.fit(X_train, y_train)
      imp = oob_dropcol_importances(rf, X_train, y_train)
      """
      rf_ = clone(rf)
      rf_.random_state = 999
      rf_.oob_score = True
      rf_.fit(X_train, y_train)
      baseline = rf_.oob_score_
      imp = []
      for col in X_train.columns:
          rf_ = clone(rf)
          rf_.random_state = 999
          rf_.oob_score = True
          rf_.fit(X_train.drop(col, axis=1), y_train)
          drop_in_score = baseline - rf_.oob_score_
          imp.append(drop_in_score)
      imp = np.array(imp)
      I = pd.DataFrame(
          data={"Feature": X_train.columns, "Importance": imp}
      )
      I = I.set_index("Feature")
      I = I.sort_values("Importance", ascending=False)
      return I
  
imports: import pandas
  import numpy
  import matplotlib.pyplot
  import sklearn
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.ensemble import RandomForestRegressor
  from distutils.version import LooseVersion
  from sklearn.model_selection import cross_val_score
  from sklearn.base import clone
  from sklearn.metrics import r2_score
  from sklearn.metrics import f1_score
  from sklearn.preprocessing import LabelEncoder
  from scipy import stats
  from pandas.api.types import is_numeric_dtype
  from matplotlib.colors import ListedColormap
  from matplotlib.ticker import FormatStrFormatter
  from copy import copy
  import warnings
  import tempfile
  from os import getpid, makedirs
label_desc: Use image node_id 8 for calling a global function with example usage: oob_dropcol_importances(rf, X_train, y_train) and returns: I