diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index ac726dd816a13..f620e6631cee9 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -2688,3 +2688,66 @@ def brier_score_loss(y_true, y_prob, *, sample_weight=None, pos_label=None):
             raise
     y_true = np.array(y_true == pos_label, int)
     return np.average((y_true - y_prob) ** 2, weights=sample_weight)
+
+
+def Fall_out(y_pred , y_true):
+    mean = (None , )
+    warn_for = "Fall Out"
+    zero_division = "warn"
+    y_pred , y_true = column_or_1d(y_pred) , column_or_1d(y_true)
+    assert_all_finite(y_pred)
+    assert_all_finite(y_true)
+    TN , FP , FN , TP = confusion_matrix(y_true, y_pred).ravel()
+    fall_out = _prf_divide(np.array([FP]),
+                np.array([FP + TN]),
+                    "Fall Out",
+                        "predicted",
+                            mean,
+                                warn_for,
+                                     zero_division
+                                                    )
+    result = fall_out[0]
+    return result
+
+
+
+def Miss_rate(y_pred , y_true):
+    mean = (None , )
+    warn_for = "Miss Rate"
+    zero_division = "warn"
+    y_pred , y_true = column_or_1d(y_pred) , column_or_1d(y_true)
+    assert_all_finite(y_pred)
+    assert_all_finite(y_true)
+    TN , FP , FN , TP = confusion_matrix(y_true, y_pred).ravel()
+    miss_rate = _prf_divide(np.array([FN]),
+               np.array([FN + TP]),
+                "Miss Rate",
+                "predicted",
+                mean,
+                warn_for,
+                zero_division
+                )
+    result = miss_rate[0]
+    return result
+
+
+
+def Specificity(y_pred , y_true):
+    mean = (None , )
+    warn_for = "Specificity"
+    zero_division = "warn"
+    y_pred , y_true = column_or_1d(y_pred) , column_or_1d(y_true)
+    assert_all_finite(y_pred)
+    assert_all_finite(y_true)
+    TN , FP , FN , TP = confusion_matrix(y_true, y_pred).ravel()
+    specificity = _prf_divide(np.array([TN]),
+                   np.array([TN + FP]),
+                    "Specificity",
+                    "predicted",
+                    mean,
+                    warn_for,
+                    zero_division
+                        )
+    result = specificity[0]
+    return result
+