code: def load_spam(split=False):
      path = os.path.join(DATA, "spam", "spam.csv")
      data = pd.read_csv(path)
  
      target = "is_spam"
      features = [col for col in data.columns if col != target]
  
      X = data[features]
      y = data[target]
      return _make_dataset(X, y, split)
  
imports: import os
  import argparse
  import numpy
  import pandas
  import matplotlib.pyplot
  from yellowbrick.features import Rank2D, RadViz
  from yellowbrick.model_selection import LearningCurve
  from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
  from yellowbrick.classifier import ClassificationReport, DiscriminationThreshold
  from yellowbrick.regressor import ResidualsPlot, PredictionError, AlphaSelection
  from collections import namedtuple
  from sklearn.datasets import make_blobs
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.cluster import MiniBatchKMeans, Birch
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LassoCV, RidgeCV, LogisticRegression
label_desc: Use image node_id 4 for calling a global function with example usage: load_spam(split) and returns: _make_dataset