diff --git a/sml/preprocessing/preprocessing.py b/sml/preprocessing/preprocessing.py
index 6acc7dc0..58a0e158 100644
--- a/sml/preprocessing/preprocessing.py
+++ b/sml/preprocessing/preprocessing.py
@@ -42,8 +42,8 @@ def label_binarize(y, *, classes, n_classes, neg_label=0, pos_label=1):
     ndarray of shape (n_samples, n_classes)
         Shape will be (n_samples, 1) for binary problems.
     """
-    eq_func = lambda x: jnp.where(classes == x, 1, 0)
-    result = jax.vmap(eq_func)(y)
+    eq_func = lambda x: classes == x
+    result = jax.vmap(eq_func)(y).astype(jnp.int_)
 
     if neg_label != 0 or pos_label != 1:
         result = jnp.where(result, pos_label, neg_label)
@@ -203,7 +203,7 @@ def binarize(X, *, threshold=0.0):
         Feature values below or equal to this are replaced by 0, above it by 1.
 
     """
-    return jnp.where(X > threshold, 1, 0)
+    return (X > threshold).astype(jnp.int_)
 
 
 class Binarizer:
@@ -626,7 +626,7 @@ def _weighted_percentile(x, q, w):
     adjusted_percentile = q / 100 * weight_cdf[-1]
 
     def searchsorted_element(x_inner):
-        encoding = jnp.where(x_inner >= weight_cdf[0:-1, 0], 1, 0)
+        encoding = x_inner >= weight_cdf[0:-1, 0]
         return jnp.sum(encoding)
 
     percentile_idx = jax.vmap(searchsorted_element)(adjusted_percentile)
@@ -1112,7 +1112,7 @@ def transform(self, X):
 
             def compute_row(bin, x, c):
                 def compute_element(x):
-                    encoding = jnp.where(x >= bin[1:-1], 1, 0)
+                    encoding = x >= bin[1:-1]
                     return jnp.clip(jnp.sum(encoding), 0, c - 2)
 
                 return jax.vmap(compute_element)(x)
@@ -1125,7 +1125,7 @@ def compute_element(x):
 
             def compute_row(bin, x):
                 def compute_element(x):
-                    encoding = jnp.where(x >= bin[1:-1], 1, 0)
+                    encoding = x >= bin[1:-1]
                     return jnp.sum(encoding)
 
                 return jax.vmap(compute_element)(x)