diff --git a/benchmarks/kd_tree.exs b/benchmarks/kd_tree.exs
deleted file mode 100644
index 2dca6bea..00000000
--- a/benchmarks/kd_tree.exs
+++ /dev/null
@@ -1,14 +0,0 @@
-# mix run benchmarks/kd_tree.exs
-Nx.global_default_backend(EXLA.Backend)
-Nx.Defn.global_default_options(compiler: EXLA)
-
-key = Nx.Random.key(System.os_time())
-{uniform, _new_key} = Nx.Random.uniform(key, shape: {1000, 3})
-
-Benchee.run(
-  %{
-    "bounded" => fn -> Scholar.Neighbors.KDTree.fit(uniform) end
-  },
-  time: 10,
-  memory_time: 2
-)
diff --git a/lib/scholar/neighbors/k_nearest_neighbors.ex b/lib/scholar/neighbors/k_nearest_neighbors.ex
index a41ee5c8..5ccb62f2 100644
--- a/lib/scholar/neighbors/k_nearest_neighbors.ex
+++ b/lib/scholar/neighbors/k_nearest_neighbors.ex
@@ -3,7 +3,9 @@ defmodule Scholar.Neighbors.KNearestNeighbors do
   The K-Nearest Neighbors.
 
   It implements both classification and regression. This implements the linear
-  version of kNN and therefore it has time complexity $O(N^2)$ for $N$ samples.
+  (brute) version of kNN and therefore it has time complexity $O(N^2)$ for $N$
+  samples. See `Scholar.Neighbors.KDTree` for an alternative implementation which
+  is also exact.
   """
   import Nx.Defn
   import Scholar.Shared
diff --git a/lib/scholar/neighbors/kd_tree.ex b/lib/scholar/neighbors/kd_tree.ex
index 1377d60e..4d33c7e3 100644
--- a/lib/scholar/neighbors/kd_tree.ex
+++ b/lib/scholar/neighbors/kd_tree.ex
@@ -3,6 +3,8 @@ defmodule Scholar.Neighbors.KDTree do
   Implements a kd-tree, a space-partitioning data structure for organizing points
   in a k-dimensional space.
 
+  It can be used to predict the K-Nearest Neighbors of a given input.
+
   This is implemented as one-dimensional tensor with indices pointed to highest
   dimension of the given tensor. Traversal starts by calling `root/0` and then
   accessing the `left_child/1` and `right_child/1`. The tree is left-balanced.
diff --git a/lib/scholar/neighbors/radius_nearest_neighbors.ex b/lib/scholar/neighbors/radius_nearest_neighbors.ex
index b1eca0e3..65181fc1 100644
--- a/lib/scholar/neighbors/radius_nearest_neighbors.ex
+++ b/lib/scholar/neighbors/radius_nearest_neighbors.ex
@@ -163,7 +163,7 @@ defmodule Scholar.Neighbors.RadiusNearestNeighbors do
 
   ## Return Values
 
-    It returns a tensor with predicted class labels
+  It returns a tensor with predicted class labels.
 
   ## Examples
 
@@ -258,7 +258,7 @@ defmodule Scholar.Neighbors.RadiusNearestNeighbors do
 
   ## Return Values
 
-    Returns indices of the selected neighbor points as a mask  (1 if a point is a neighbor, 0 otherwise) and their respective distances.
+  Returns indices of the selected neighbor points as a mask  (1 if a point is a neighbor, 0 otherwise) and their respective distances.
 
   ## Examples