diff --git a/python/cuml/cuml/manifold/umap/umap.pyx b/python/cuml/cuml/manifold/umap/umap.pyx
index 0aea8e1fe6..69db8a1d97 100644
--- a/python/cuml/cuml/manifold/umap/umap.pyx
+++ b/python/cuml/cuml/manifold/umap/umap.pyx
@@ -715,6 +715,23 @@ class UMAP(Base, InteropMixin, CMajorInputTagMixin, SparseInputTagMixin):
         More specific parameters controlling the embedding. If None these
         values are set automatically as determined by ``min_dist`` and
         ``spread``.
+    target_n_neighbors: int (optional, default=-1)
+        The number of nearest neighbors to use to construct the target
+        simplicial set. If set to -1 use the ``n_neighbors`` value.
+    target_metric: string (optional, default='categorical')
+        The metric used to measure distance for a target array when using
+        supervised dimension reduction. By default this is 'categorical'
+        which will measure distance in terms of whether categories match
+        or are different. Furthermore, if semi-supervised is required
+        target values of -1 will be treated as unlabelled under the
+        'categorical' metric. If the target array takes continuous values
+        (e.g. for a regression problem) then metric of 'euclidean' or 'l2'
+        is probably more appropriate.
+    target_weight: float (optional, default=0.5)
+        Weighting factor between data topology and target topology. A
+        value of 0.0 weights predominantly on data, a value of 1.0 places
+        a strong emphasis on target. The default of 0.5 balances the
+        weighting equally between data and target.
     hash_input: bool, optional (default = False)
         UMAP can hash the training input so that exact embeddings
         are returned when transform is called on the same data upon
diff --git a/python/cuml/cuml/neighbors/nearest_neighbors.pyx b/python/cuml/cuml/neighbors/nearest_neighbors.pyx
index 98f92153bb..a965090b33 100644
--- a/python/cuml/cuml/neighbors/nearest_neighbors.pyx
+++ b/python/cuml/cuml/neighbors/nearest_neighbors.pyx
@@ -470,9 +470,18 @@ class NearestNeighbors(Base,
           partial information allowing faster distances calculations
 
     metric : string (default='euclidean').
-        Distance metric to use. Supported distances are ['l1, 'cityblock',
-        'taxicab', 'manhattan', 'euclidean', 'l2', 'braycurtis', 'canberra',
-        'minkowski', 'chebyshev', 'jensenshannon', 'cosine', 'correlation']
+        Distance metric to use. Supported metrics include: 'l1', 'cityblock',
+        'taxicab', 'manhattan', 'euclidean', 'l2', 'sqeuclidean', 'canberra',
+        'minkowski', 'lp', 'chebyshev', 'linf', 'jensenshannon', 'cosine',
+        'braycurtis', 'jaccard', 'hellinger', 'correlation', 'inner_product'.
+        The ``'ivfflat'`` and ``'ivfpq'``
+        algorithms only support: 'euclidean', 'l2', 'sqeuclidean', 'cosine',
+        'correlation', 'inner_product', whereas the ``'rbc'`` algorithm only
+        supports 'euclidean', 'l2', and 'haversine' (≤3 dimensions only).
+        For sparse inputs, only the ``'brute'`` algorithm is supported, with
+        metrics: 'l1', 'cityblock', 'taxicab', 'manhattan', 'euclidean', 'l2',
+        'canberra', 'minkowski', 'lp', 'chebyshev', 'linf', 'cosine',
+        'inner_product', 'jaccard', 'hellinger'.
     p : float (default=2)
         Parameter for the Minkowski metric. When p = 1, this is equivalent to
         manhattan distance (l1), and euclidean distance (l2) for p = 2. For