Updated the documentation on distance_threshold restrictions

scikit-learn · Aug 7, 2017 · 1c9ad69 · 1c9ad69
1 parent b4bc8bc
commit 1c9ad69
Showing 1 changed file with 7 additions and 2 deletions.
diff --git a/sklearn/cluster/hierarchical.py b/sklearn/cluster/hierarchical.py
@@ -568,6 +568,9 @@ def _hc_cut(n_clusters, children, n_leaves,
         If ``distance_threshold`` is set then ``n_clusters`` will be the
         number of clusters at which the distances exceed the
         ``distance_threshold``
+        NOTE: You should set either ``n_clusters`` or ``distance_threshold``,
+        NOT both. If the ``distance_threshold`` is set then ``n_clusters`` is
+        ignored.
 
     distances : ndarray, shape (n_nodes-1,) (optional)
         Only used when distance_threshold is set.
@@ -626,7 +629,8 @@ class AgglomerativeClustering(BaseEstimator, ClusterMixin):
     distance_threshold : int (optional)
         The distance threshold to cluster at.
         NOTE: You should set either ``n_clusters`` or ``distance_threshold``,
-        NOT both.
+        NOT both. If the ``distance_threshold`` is set then ``n_clusters`` is
+        ignored.
 
     affinity : string or callable, default: "euclidean"
         Metric used to compute the linkage. Can be "euclidean", "l1", "l2",
@@ -829,7 +833,8 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
     distance_threshold : int (optional)
         The distance threshold to cluster at.
         NOTE: You should set either ``n_clusters`` or ``distance_threshold``,
-        NOT both.
+        NOT both. If the ``distance_threshold`` is set then ``n_clusters`` is
+        ignored.
 
     affinity : string or callable, default "euclidean"
         Metric used to compute the linkage. Can be "euclidean", "l1", "l2",