typo

train.py

@@ -30,6 +30,7 @@ seeds = [231964, 48928, 132268, 113986, 574626, 130068, 226585, 446306, 535997,
 # save metrics the test split for the best combinations of ML hyperparameters
 #  * Hyperparameters GridSearch for each ML Model for up to 60 different combinations
 #  * 10 different Machine Learning Models capable of Binary Clasification
+#  * Oversample training data if unbalanced
 #  * Model trained on data with no missing values, and impute MICE and KNN
 #  * Different random train and test splits, for given test_size ratio
 # A folder is created with the label name with all the state and run data

trainer.py

@@ -743,7 +743,7 @@ class BinaryTuner:
 
     def wrap_and_save(self):
         self.logger.info("{:=^60}".format(' Saving Summary and Wrap the output in a ZipFile '))
-        for metric in ["ROC_AUC", "NPV", "PPV", "Brier", "sensitivity", "specificity"]:
+        for metric in ["ROC_AUC", "NPV", "PPV", "Brier", "Sensitivity", "Specificity"]:
             with pd.ExcelWriter('{}/Summary-{}.xlsx'.format(self.name, metric) , engine='xlsxwriter') as xls:
                 self.get_best_models(metric).to_excel(xls, sheet_name='Results')