code: def select_features_example(
      algorithm="isomap",
      path="images/occupancy_select_k_best_isomap_manifold.png",
      **kwargs,
  ):
      _, ax = plt.subplots(figsize=(9, 6))
  
      X, y = load_occupancy()
      classes = ["unoccupied", "occupied"]
  
      model = Pipeline(
          [
              ("selectk", SelectKBest(k=3, score_func=f_classif)),
              (
                  "viz",
                  Manifold(
                      ax=ax,
                      manifold=algorithm,
                      classes=classes,
                      **kwargs,
                  ),
              ),
          ]
      )
  
      model.fit_transform(X, y)
      model.named_steps["viz"].show(outpath=path)
  
file_path: yellowbrick/docs/api/features/manifold.py
imports: import os
  import matplotlib.pyplot
  from sklearn import datasets
  from sklearn.pipeline import Pipeline
  from sklearn.feature_selection import f_classif
  from sklearn.feature_selection import SelectKBest
  from yellowbrick.datasets import load_occupancy, load_concrete
  from yellowbrick.features.manifold import Manifold, MANIFOLD_ALGORITHMS, manifold_embedding
label_desc: Use image node_id 2 for calling a global function with example usage: select_features_example(algorithm, path) without return types