Fri Aug  4 20:25:30 2023

cancer_classify_svm_rbf():
  Python version: 3.8.10
  scikit-learn version: 1.3.0
  Classify data from the cancer dataset.
  Use the support vector classifier with the RBF kernel.
  Rescale the data to improve the fitting of the test data.

  Retrieve the cancer dataset, (X, y).
  Training accuracy =  0.903755868544601
  Testing accuracy  =  0.9370629370629371
  Graphics saved as "cancer_classify_svm_rbf_feature_range"

Rescale the training data:

  Minimum for each feature =  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
 0. 0. 0. 0. 0. 0.]
  Maximum for each feature =  [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
 1. 1. 1. 1. 1. 1.]
  Scaled training accuracy =  0.9835680751173709
  Scaled testing accuracy  =  0.972027972027972

Now try SVC with C = 1000 to fit a more complex model.
  Scaled training accuracy =  1.0
  Scaled testing accuracy  =  0.958041958041958

cancer_classify_svm_rbf():
  Normal end of execution.
Fri Aug  4 20:25:32 2023