code: def model(opt):
      gbc = GradientBoostingClassifier(
          n_estimators=opt["n_estimators"],
          max_depth=opt["max_depth"],
          min_samples_split=opt["min_samples_split"],
          min_samples_leaf=opt["min_samples_leaf"],
      )
      filter_ = SelectKBest(f_classif, k=opt["k"])
      model_ = opt["pipeline"](filter_, gbc)
  
      scores = cross_val_score(model_, X, y, cv=3)
  
      return scores.mean()
  
file_path: Hyperactive/examples/optimization_applications/sklearn_pipeline_example.py
imports: from sklearn.datasets import load_breast_cancer
  from sklearn.model_selection import cross_val_score
  from sklearn.feature_selection import SelectKBest, f_classif
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.pipeline import Pipeline
  from hyperactive import Hyperactive
label_desc: Use image node_id 3 for calling a global function with example usage: model(opt) and returns: scores