Using Pycaret library for autoML

pycaret.py

from pycaret.datasets import get_data
from pycaret.classification import *
report["Scores"] = np.round(report["Scores"], 3)
report.sort_values(by = "Scores", ascending = False, inplace = True)
#report.index
ga_feats = report.iloc[0]["Chosen Feats"]
ename = setup(data = D[used_feats], target = "DEATH_EVENT", 
              test_data = None,
              fold_strategy = "stratifiedkfold",
              fold_shuffle = True, 
              use_gpu = True,
              normalize = True,
              categorical_features = None,
              
              #pca = True,
              #pca_method = "kernel",
              #pca_components = 5, 
              preprocess = False,
              html = True,
              #POLYNOMIAL
              #polynomial_features = True,
              #polynomial_degree = 2, 
              #transformation
              
              #transformation = True,
              #feature_selection = True, 
              feature_interaction = True,
          
              fix_imbalance = True,
              #fix_imbalance_method  = imblearn.over_sampling.SMOTE(),
              imputation_type='simple',
              verbose = True,
              )


rskf = RepeatedStratifiedKFold(n_splits = 5, n_repeats = 20)
best_model = compare_models(sort = "MCC", round = 2, 
                            fold = rskf,
                            #include = ["rf","catboost"],
                            #n_select = 1
                            )