Simplify model selection logic

src/processing.py

@@ -10,35 +10,15 @@ from sklearn.tree import DecisionTreeClassifier
 from preprocessing import parse_data, split_k_sets
 
 
-def naive_bayes():
-    model = GaussianNB()
-    return model
-
-
-def linear_svc():
-    model = LinearSVC(random_state=42)
-    return model
-
-
-def k_nearest_neighbors():
-    model = KNeighborsClassifier(n_neighbors=10)
-    return model
-
-
-def decision_tree():
-    model = DecisionTreeClassifier(random_state=42)
-    return model
-
-
 def choose_model(model):
     if model == "gnb":
-        return naive_bayes()
+        return GaussianNB()
     elif model == "svc":
-        return linear_svc()
+        return LinearSVC(random_state=42)
     elif model == "knn":
-        return k_nearest_neighbors()
+        return KNeighborsClassifier(n_neighbors=10)
     elif model == "tree":
-        return decision_tree()
+        return DecisionTreeClassifier(random_state=42)
 
 
 def predict_data(data, target, model):