diff --git a/aisdc/attacks/structural_attack.py b/aisdc/attacks/structural_attack.py
index 76f32453..62bc08a5 100644
--- a/aisdc/attacks/structural_attack.py
+++ b/aisdc/attacks/structural_attack.py
@@ -25,7 +25,7 @@
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# pylint: disable=too-many-boolean-expressions,chained-comparison
+# pylint: disable=chained-comparison
 
 
 def get_unnecessary_risk(model: BaseEstimator) -> bool:
@@ -73,7 +73,7 @@ def _get_unnecessary_risk_dt(model: DecisionTreeClassifier) -> bool:
     min_samples_leaf = model.min_samples_leaf
     min_samples_split = model.min_samples_split
     splitter = model.splitter
-    if (
+    return (
         (max_depth > 7.5 and min_samples_leaf <= 7.5 and min_samples_split <= 15)
         or (
             splitter == "best"
@@ -99,9 +99,7 @@ def _get_unnecessary_risk_dt(model: DecisionTreeClassifier) -> bool:
             and min_samples_leaf <= 7.5
             and max_features is None
         )
-    ):
-        return True
-    return False
+    )
 
 
 def _get_unnecessary_risk_rf(model: RandomForestClassifier) -> bool:
@@ -111,7 +109,7 @@ def _get_unnecessary_risk_rf(model: RandomForestClassifier) -> bool:
     max_features = model.max_features
     min_samples_leaf = model.min_samples_leaf
     min_samples_split = model.min_samples_split
-    if (
+    return (
         (max_depth > 3.5 and n_estimators > 35 and max_features is not None)
         or (
             max_depth > 3.5
@@ -126,9 +124,7 @@ def _get_unnecessary_risk_rf(model: RandomForestClassifier) -> bool:
             and min_samples_leaf <= 15
             and not model.bootstrap
         )
-    ):
-        return True
-    return False
+    )
 
 
 def _get_unnecessary_risk_xgb(model: XGBClassifier) -> bool:
@@ -141,13 +137,11 @@ def _get_unnecessary_risk_xgb(model: XGBClassifier) -> bool:
     n_estimators = int(model.n_estimators) if model.n_estimators else 100
     max_depth = float(model.max_depth) if model.max_depth else 6
     min_child_weight = float(model.min_child_weight) if model.min_child_weight else 1.0
-    if (
+    return (
         (max_depth > 3.5 and 3.5 < n_estimators <= 12.5 and min_child_weight <= 1.5)
         or (max_depth > 3.5 and n_estimators > 12.5 and min_child_weight <= 3)
         or (max_depth > 3.5 and n_estimators > 62.5 and 3 < min_child_weight <= 6)
-    ):
-        return True
-    return False
+    )
 
 
 def get_tree_parameter_count(dtree: DecisionTreeClassifier) -> int: