diff --git a/cpp/src/hdbscan/detail/soft_clustering.cuh b/cpp/src/hdbscan/detail/soft_clustering.cuh
index 10085d0e49..2ace77f5d0 100644
--- a/cpp/src/hdbscan/detail/soft_clustering.cuh
+++ b/cpp/src/hdbscan/detail/soft_clustering.cuh
@@ -105,7 +105,7 @@ void dist_membership_vector(const raft::handle_t& handle,
         distance<raft::distance::DistanceType::CosineExpanded, value_t, value_t, value_t, int>(
           handle, query + batch_offset * n, exemplars_dense.data(), dist.data(), samples_per_batch, n_exemplars, n, true);
       break;
-    default: ASSERT(false, "Incorrect metric passed!");
+    default: RAFT_EXPECTS(false, "Incorrect metric passed!");
   }
 
   // compute the minimum distances to exemplars of each cluster
@@ -396,7 +396,8 @@ void all_points_membership_vectors(const raft::handle_t& handle,
   size_t n = prediction_data.n_cols;
 
   if (batch_size > m) batch_size = m;
-  RAFT_EXPECTS(0 < batch_size && batch_size <= m, "Invalid batch_size. batch_size should be > 0 and <= the number of samples in the training data");
+  RAFT_EXPECTS(0 < batch_size && batch_size <= m,
+               "Invalid batch_size. batch_size should be > 0 and <= the number of samples in the training data");
 
   auto parents    = condensed_tree.get_parents();
   auto children   = condensed_tree.get_children();
@@ -522,7 +523,8 @@ void membership_vector(const raft::handle_t& handle,
   value_t* lambdas               = condensed_tree.get_lambdas();
 
   if (batch_size > n_prediction_points) batch_size = n_prediction_points;
-  RAFT_EXPECTS(0 < batch_size && batch_size <= n_prediction_points, "Invalid batch_size. batch_size should be > 0 and <= the number of samples in the training data");
+  RAFT_EXPECTS(0 < batch_size && batch_size <= n_prediction_points,
+               "Invalid batch_size. batch_size should be > 0 and <= the number of prediction points");
 
   rmm::device_uvector<value_t> dist_membership_vec(n_prediction_points * n_selected_clusters,
                                                    stream);
diff --git a/python/cuml/cluster/hdbscan/prediction.pyx b/python/cuml/cluster/hdbscan/prediction.pyx
index c3db0be8d7..6a6803b186 100644
--- a/python/cuml/cluster/hdbscan/prediction.pyx
+++ b/python/cuml/cluster/hdbscan/prediction.pyx
@@ -146,12 +146,12 @@ def all_points_membership_vectors(clusterer, batch_size=4096):
         had ``prediction_data=True`` set.
 
     batch_size : int, optional, default=min(4096, n_rows)
-        Lowers memory requirement by computing distance-based membership in
-        smaller batches of points in the training data. Batch size of 0 uses
-        all of the training points, batch size of 1000 computes distances for
-        1000 points at a time. The default batch_size is 4096. If the number
-        of rows in the original dataset is less than 4096, this defaults to
-        the number of rows.
+        Lowers memory requirement by computing distance-based membership
+        in smaller batches of points in the training data. A batch size
+        of 1000 computes distance based memberships for 1000 points at a
+        time. The default batch size is 4096. If the number of rows in
+        the original dataset is less than 4096, this defaults to the
+        number of rows.
 
     Returns
     -------
@@ -251,12 +251,12 @@ def membership_vector(clusterer, points_to_predict, batch_size=4096, convert_dty
         clusterer was fit.
     
     batch_size : int, optional, default=min(4096, n_points_to_predict)
-        Lowers memory requirement by computing distance-based membership in
-        smaller batches of points in the training data. Batch size of 0 uses
-        all of the training points, batch size of 1000 computes distances for
-        1000 points at a time. The default batch_size is 4096. If the number
-        of rows in the original dataset is less than 4096, this defaults to
-        the number of rows.
+        Lowers memory requirement by computing distance-based membership
+        in smaller batches of points in the prediction data. A batch size
+        of 1000 computes distance based memberships for 1000 points at a
+        time. The default batch_size is 4096. If the number of rows in
+        the prediction dataset is less than 4096, this defaults to the
+        number of rows.
 
     Returns
     -------