diff --git a/model_selection/cv_metric_gen.py b/model_selection/cv_metric_gen.py
index 7bd4c3cacebe41c12d1043d1e312811cc5e5c505..16c48bb1085556f0f60cd0abb37416a34f8523ca 100644
--- a/model_selection/cv_metric_gen.py
+++ b/model_selection/cv_metric_gen.py
@@ -113,26 +113,25 @@ def TN_scorer(clf, X, y):
     """Gives the number of samples predicted as true negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TN = cm[0,0]
-    return TN
+    return cm[0, 0]
+
 def FN_scorer(clf, X, y):
     """Gives the number of samples predicted as false negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FN = cm[0,1]
-    return FN
+    return cm[1, 0]
+
 def FP_scorer(clf, X, y):
-    """Gives the number of samples predicted as false positive"""
+    """Gives the number of samples predicted as false positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FP = cm[1,0]
-    return FP
+    return cm[0, 1]
+
 def TP_scorer(clf, X, y):
-    """Gives the number of samples predicted as true positive"""
+    """Gives the number of samples predicted as true positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TP = cm[1,1]
-    return TP
+    return cm[1, 1]
 
 def negative_recall_scorer(clf, X, y):
     """Gives the negative recall defined as the (number of true_negative_samples)/(total number of negative samples)"""
diff --git a/model_selection/test_models.py b/model_selection/test_models.py
index 2cf3f1e32ca8c93b4ae6850300f98f3ddb95df07..8ab1b46a3a2cb03e4367de38fe8ad45d98937ff2 100644
--- a/model_selection/test_models.py
+++ b/model_selection/test_models.py
@@ -124,26 +124,25 @@ def TN_scorer(clf, X, y):
     """Gives the number of samples predicted as true negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TN = cm[0,0]
-    return TN
+    return cm[0, 0]
+
 def FN_scorer(clf, X, y):
     """Gives the number of samples predicted as false negatives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FN = cm[0,1]
-    return FN
+    return cm[1, 0]
+
 def FP_scorer(clf, X, y):
-    """Gives the number of samples predicted as false positive"""
+    """Gives the number of samples predicted as false positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    FP = cm[1,0]
-    return FP
+    return cm[0, 1]
+
 def TP_scorer(clf, X, y):
-    """Gives the number of samples predicted as true positive"""
+    """Gives the number of samples predicted as true positives"""
     y_pred = clf.predict(X)
     cm = confusion_matrix(y, y_pred)
-    TP = cm[1,1]
-    return TP
+    return cm[1, 1]
 
 def negative_recall_scorer(clf, X, y):
     """Gives the negative recall defined as the (number of true_negative_samples)/(total number of negative samples)"""