added MultClassPipe2 that has one hot encoder step to the pipeline

MultClassPipe.py

@@ -23,6 +23,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
 #%%
 rs = {'random_state': 42}
+# TODO: add preprocessing step with one hot encoder
 
 # Multiple Classification - Model Pipeline
 def MultClassPipeline(X_train, X_test, y_train, y_test):
@@ -35,6 +36,15 @@ def MultClassPipeline(X_train, X_test, y_train, y_test):
     dt = DecisionTreeClassifier(**rs)
     et = ExtraTreesClassifier(**rs)
     rf = RandomForestClassifier(**rs)
+    rf2 = RandomForestClassifier(
+                          min_samples_leaf=50,
+                          n_estimators=150,
+                          bootstrap=True,
+                          oob_score=True,
+                          n_jobs=-1,
+                          random_state=42,
+                          max_features='auto')
+    
     xgb = XGBClassifier(**rs, verbosity=0)
 
     clfs = [
@@ -46,6 +56,7 @@ def MultClassPipeline(X_train, X_test, y_train, y_test):
             ('Decision Tree', dt), 
             ('Extra Trees', et), 
             ('Random Forest', rf), 
+            ('Random Forest2', rf2), 
             ('XGBoost', xgb)
             ]