diff --git a/modules/machine_learning/classification.py b/modules/machine_learning/classification.py
index 8607a124b7038d4c0a6108800dadb3bb156ac8ac..035ef544625b7c364f2f72b4c2176ea7b58b4da5 100644
--- a/modules/machine_learning/classification.py
+++ b/modules/machine_learning/classification.py
@@ -60,12 +60,12 @@ def logistic_regression(x, y):
     yhat_prob = LR.predict_proba(x_test)
 
     print(confusion_matrix(y_test, yhat, labels=[1,0]))
-    print('Accuracy:    ', accuracy_score(y_test, yhat))
-    print('Jaccard Idx: ', jaccard_score(y_test, yhat))
-    print('Precision:   ', precision_score(y_test, yhat))
-    print('Recall:      ', recall_score(y_test, yhat))
-    print('F1:          ', f1_score(y_test, yhat))
-    print('AUC:         ', roc_auc_score(y_test, yhat_prob[:, 1]))
+    print('Accuracy:    ', "{:.4f}".format(accuracy_score(y_test, yhat)))
+    print('Jaccard Idx: ', "{:.4f}".format(jaccard_score(y_test, yhat)))
+    print('Precision:   ', "{:.4f}".format(precision_score(y_test, yhat)))
+    print('Recall:      ', "{:.4f}".format(recall_score(y_test, yhat)))
+    print('F1:          ', "{:.4f}".format(f1_score(y_test, yhat)))
+    print('AUC:         ', "{:.4f}".format(roc_auc_score(y_test, yhat_prob[:, 1])))
 
 
 def k_nearest_neighbors(x, y, k=4):
@@ -82,10 +82,9 @@ def k_nearest_neighbors(x, y, k=4):
     yhat = KN_C.predict(x_test)
     yhat_prob = KN_C.predict_proba(x_test)
 
-    print(confusion_matrix(y_test, yhat, labels=[1,0]))
-    print('Accuracy:    ', accuracy_score(y_test, yhat))
-    print('Jaccard Idx: ', jaccard_score(y_test, yhat))
-    print('Precision:   ', precision_score(y_test, yhat))
-    print('Recall:      ', recall_score(y_test, yhat))
-    print('F1:          ', f1_score(y_test, yhat))
-    print('AUC:         ', roc_auc_score(y_test, yhat_prob[:, 1]))
\ No newline at end of file
+    print('Accuracy:    ', "{:.4f}".format(accuracy_score(y_test, yhat)))
+    print('Jaccard Idx: ', "{:.4f}".format(jaccard_score(y_test, yhat)))
+    print('Precision:   ', "{:.4f}".format(precision_score(y_test, yhat)))
+    print('Recall:      ', "{:.4f}".format(recall_score(y_test, yhat)))
+    print('F1:          ', "{:.4f}".format(f1_score(y_test, yhat)))
+    print('AUC:         ', "{:.4f}".format(roc_auc_score(y_test, yhat_prob[:, 1])))
\ No newline at end of file