code: def create_dataset(
      n_samples=1000,
      weights=(0.01, 0.01, 0.98),
      n_classes=3,
      class_sep=0.8,
      n_clusters=1,
  ):
      return make_classification(
          n_samples=n_samples,
          n_features=2,
          n_informative=2,
          n_redundant=0,
          n_repeated=0,
          n_classes=n_classes,
          n_clusters_per_class=n_clusters,
          weights=list(weights),
          class_sep=class_sep,
          random_state=0,
      )
  
file_path: imbalanced-learn/examples/over-sampling/plot_comparison_over_sampling.py
imports: import matplotlib.pyplot
  import seaborn
  from sklearn.datasets import make_classification
  import numpy
  from sklearn.linear_model import LogisticRegression
  from imblearn.over_sampling import RandomOverSampler
  from imblearn.pipeline import make_pipeline
  from imblearn import FunctionSampler
  from imblearn.over_sampling import ADASYN, SMOTE
  from sklearn.cluster import MiniBatchKMeans
  from imblearn.over_sampling import SVMSMOTE, BorderlineSMOTE, KMeansSMOTE
  from collections import Counter
  from imblearn.over_sampling import SMOTENC
  from imblearn.over_sampling import SMOTEN
label_desc: Use image node_id 1 for calling a global function with example usage: create_dataset(n_samples, weights, n_classes, class_sep, n_clusters) and returns: make_classification