From 632a9b3d69700b9db75397654974ceab0602bdf9 Mon Sep 17 00:00:00 2001
From: tilakrayal <81610181+tilakrayal@users.noreply.github.com>
Date: Wed, 18 Sep 2024 14:24:32 +0530
Subject: [PATCH] Update classification_with_tfdf.md

---
 examples/structured_data/md/classification_with_tfdf.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/structured_data/md/classification_with_tfdf.md b/examples/structured_data/md/classification_with_tfdf.md
index d870c0d72b..090adc4065 100644
--- a/examples/structured_data/md/classification_with_tfdf.md
+++ b/examples/structured_data/md/classification_with_tfdf.md
@@ -1185,15 +1185,15 @@ class BinaryTargetEncoding(layers.Layer):
 
         # Filter the data where the target label is positive.
         positive_indices = tf.where(condition=target_values)
-        postive_feature_values = tf.gather_nd(
+        positive_feature_values = tf.gather_nd(
             params=feature_values, indices=positive_indices
         )
         # Compute how many times each feature value occurred with a positive target label.
         positive_frequency = tf.math.unsorted_segment_sum(
             data=tf.ones(
-                shape=(postive_feature_values.shape[0], 1), dtype=tf.dtypes.float64
+                shape=(positive_feature_values.shape[0], 1), dtype=tf.dtypes.float64
             ),
-            segment_ids=postive_feature_values,
+            segment_ids=positive_feature_values,
             num_segments=self.vocabulary_size,
         )