diff --git a/docs/posts/classification-metrics-on-the-backend/index.qmd b/docs/posts/classification-metrics-on-the-backend/index.qmd
index 5ce1228438cb..dd389838b595 100644
--- a/docs/posts/classification-metrics-on-the-backend/index.qmd
+++ b/docs/posts/classification-metrics-on-the-backend/index.qmd
@@ -40,9 +40,8 @@ The easiest way to break down how this works is to look at a confusion matrix.
 
 A confusion matrix is a table used to describe the performance of a classification
 model on a set of data for which the true values are known. As binary classification
-only involves two categories, the confusion matrix is a simple 2x2 table where each
-cell shows the count of true positives, false positives, false negatives, and true
-negatives.
+only involves two categories, the confusion matrix is a simple 2x2 table of true
+positives, false positives, false negatives, and true negatives.
 
 ![](confusion_matrix.png)