diff --git a/lib/scholar/shared.ex b/lib/scholar/shared.ex
index d7a237e0..f36a5e53 100644
--- a/lib/scholar/shared.ex
+++ b/lib/scholar/shared.ex
@@ -59,7 +59,9 @@ defmodule Scholar.Shared do
         Nx.tensor(weights, type: type)
 
       Nx.is_tensor(weights) and Nx.shape(weights) in [{}, {num_samples}] ->
-        weights |> Nx.broadcast({num_samples}) |> Nx.as_type(type)
+        weights = Nx.broadcast(weights, {num_samples})
+        # :type is optional, and the branches above already work without it
+        if type, do: Nx.as_type(weights, type), else: weights
 
       true ->
         raise ArgumentError,
diff --git a/test/scholar/metrics/classification_test.exs b/test/scholar/metrics/classification_test.exs
index 975abed0..5ba7946d 100644
--- a/test/scholar/metrics/classification_test.exs
+++ b/test/scholar/metrics/classification_test.exs
@@ -4,6 +4,40 @@ defmodule Scholar.Metrics.ClassificationTest do
   alias Scholar.Metrics.Classification
   doctest Classification
 
+  test "confusion_matrix - sample weights as a tensor match the same weights as a list" do
+    y_true = Nx.tensor([0, 0, 1, 1], type: :u32)
+    y_pred = Nx.tensor([0, 1, 1, 1], type: :u32)
+
+    from_list =
+      Classification.confusion_matrix(y_true, y_pred,
+        num_classes: 2,
+        sample_weights: [1, 2, 1, 1]
+      )
+
+    from_tensor =
+      Classification.confusion_matrix(y_true, y_pred,
+        num_classes: 2,
+        sample_weights: Nx.tensor([1, 2, 1, 1])
+      )
+
+    assert from_tensor == from_list
+  end
+
+  test "confusion_matrix - a scalar sample weight applies to every sample" do
+    y_true = Nx.tensor([0, 0, 1, 1], type: :u32)
+    y_pred = Nx.tensor([0, 1, 1, 1], type: :u32)
+
+    weighted =
+      Classification.confusion_matrix(y_true, y_pred,
+        num_classes: 2,
+        sample_weights: Nx.tensor(2)
+      )
+
+    unweighted = Classification.confusion_matrix(y_true, y_pred, num_classes: 2)
+
+    assert Nx.to_flat_list(weighted) == Nx.to_flat_list(Nx.multiply(unweighted, 2))
+  end
+
   test "roc_curve - y_score with repeated elements" do
     y_score = Nx.tensor([0.1, 0.1, 0.2, 0.2, 0.3, 0.3])
     y_true = Nx.tensor([0, 0, 1, 1, 1, 1])