DOC Fix det_curve docstring (scikit-learn#34390)

Author: sha-sta

sklearn/metrics/_ranking.py

@@ -367,11 +367,11 @@ class or non-thresholded decision values (as returned by
 
     fnr : ndarray of shape (n_thresholds,)
         False negative rate (FNR) such that element i is the false negative
-        rate of predictions with score >= thresholds[i]. This is occasionally
+        rate of predictions with score < thresholds[i]. This is occasionally
         referred to as false rejection or miss rate.
 
     thresholds : ndarray of shape (n_thresholds,)
-        Decreasing thresholds on the decision function (either `predict_proba`
+        Increasing thresholds on the decision function (either `predict_proba`
         or `decision_function`) used to compute FPR and FNR.
 
         .. versionchanged:: 1.7

sklearn/metrics/tests/test_ranking.py

@@ -1383,10 +1383,11 @@ def test_score_scale_invariance():
 )
 def test_det_curve_toydata(y_true, y_score, expected_fpr, expected_fnr):
     # Check on a batch of small examples.
-    fpr, fnr, _ = det_curve(y_true, y_score)
+    fpr, fnr, thresholds = det_curve(y_true, y_score)
 
     assert_allclose(fpr, expected_fpr)
     assert_allclose(fnr, expected_fnr)
+    assert np.all(np.diff(thresholds) >= 0)
 
 
 @pytest.mark.parametrize(