trying under and oversampling

MultClassPipe3.py

@@ -60,6 +60,18 @@ from sklearn.ensemble import AdaBoostClassifier
 from imblearn.combine import SMOTEENN
 from imblearn.under_sampling import EditedNearestNeighbours
 
+from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
+from sklearn.neural_network import MLPClassifier
+
+from sklearn.linear_model import RidgeClassifier, SGDClassifier, PassiveAggressiveClassifier
+from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
+from sklearn.svm import SVC
+from xgboost import XGBClassifier
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.linear_model import SGDClassifier
+from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
+
+
 #%%
 rs = {'random_state': 42}
 njobs = {'n_jobs': 10}
@@ -122,8 +134,7 @@ def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categ
     mlp     = MLPClassifier(max_iter = 500, **rs)
     dt      = DecisionTreeClassifier(**rs)
     et      = ExtraTreesClassifier(**rs)
-    rf      = RandomForestClassifier(**rs,
-                                    n_estimators     = 1000 )
+    rf      = RandomForestClassifier(**rs, n_estimators = 1000 )
     rf2     = RandomForestClassifier(
                           min_samples_leaf = 5
                           , n_estimators     = 100 #10
@@ -136,7 +147,7 @@ def MultClassPipeSKFCV(input_df, target, skf_cv, var_type = ['numerical', 'categ
                         
     lda = LinearDiscriminantAnalysis()
     
-    mnb = MultinomialNB(**rs)
+    mnb = MultinomialNB()
     
     pa  = PassiveAggressiveClassifier(**rs, **njobs)