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model_selection/hyperparam_tuning.py

+# Hyperparameter Tuning
+# Author: Joaquín Torres Bravo
 """
     Finding optimal hyperparameters through RandomSearchCV for each group (1. pre - 2. post) 
-    and method: 
+    and pipeline: 
         1. Original training dataset
-        2. Original training dataset - Cost sensitive
+        2. Original training dataset with cost-sensitive learning (penalizing misclassification of minority class)
         3. Oversampling
         4. Undersampling
 """
@@ -11,16 +13,18 @@
 # --------------------------------------------------------------------------------------------------------
 import pandas as pd
 import numpy as np
-
+# Models
 from xgboost import XGBClassifier
-from sklearn.model_selection import StratifiedKFold
 from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier
 from sklearn.neural_network import MLPClassifier
 from sklearn.svm import SVC
 from sklearn.linear_model import  LogisticRegression
 from sklearn.tree import DecisionTreeClassifier
-from scipy.stats import randint, uniform
+# CV
+from sklearn.model_selection import StratifiedKFold
 from sklearn.model_selection import RandomizedSearchCV
+# Misc
+from scipy.stats import randint, uniform
 # --------------------------------------------------------------------------------------------------------
 
 # Function to read training datasets