Tue Jul 18 22:20:03 2023

cancer_classify_decision():
  Python version: 3.8.10
  scikit-learn version: 1.2.2

  Retrieve the cancer dataset, (X, y).

  Create an unpruned decision tree:
    Accuracy on training set =  1.0
    Accuracy on test set     =  0.9370629370629371
  Graphics saved as "unpruned_tree.png"

  Create a pruned decision tree (max depth = 4 ):
    Accuracy on training set =  0.9882629107981221
    Accuracy on test set     =  0.951048951048951
  Graphics saved as "pruned_tree.png"

  Feature importance:
[0.         0.         0.         0.         0.         0.
 0.         0.         0.         0.         0.01019737 0.04839825
 0.         0.         0.0024156  0.         0.         0.
 0.         0.         0.72682851 0.0458159  0.         0.
 0.0141577  0.         0.018188   0.1221132  0.01188548 0.        ]
  Graphics saved as feature_importance.png"

cancer_classify_decision():
  Normal end of execution.
Tue Jul 18 22:20:04 2023