From 9a3c79e0a8d8c42daa6c2421f0f386d037cfe8ec Mon Sep 17 00:00:00 2001
From: rafijacSense <94111907+rafijacSense@users.noreply.github.com>
Date: Sun, 7 Apr 2024 06:28:45 -0400
Subject: [PATCH] switched val to test

---
 website/docs/Use-Cases/Task-Oriented-AutoML.md | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/website/docs/Use-Cases/Task-Oriented-AutoML.md b/website/docs/Use-Cases/Task-Oriented-AutoML.md
index 97d64010d4..f7b4e5da87 100644
--- a/website/docs/Use-Cases/Task-Oriented-AutoML.md
+++ b/website/docs/Use-Cases/Task-Oriented-AutoML.md
@@ -74,8 +74,8 @@ The optimization metric is specified via the `metric` argument. It can be either
 
 ```python
 def custom_metric(
-    X_val,
-    y_val,
+    X_test,
+    y_test,
     estimator,
     labels,
     X_train,
@@ -93,8 +93,8 @@ For example,
 
 ```python
 def custom_metric(
-    X_val,
-    y_val,
+    X_test,
+    y_test,
     estimator,
     labels,
     X_train,
@@ -107,9 +107,9 @@ def custom_metric(
     import time
 
     start = time.time()
-    y_pred = estimator.predict_proba(X_val)
-    pred_time = (time.time() - start) / len(X_val)
-    val_loss = log_loss(y_val, y_pred, labels=labels, sample_weight=weight_val)
+    y_pred = estimator.predict_proba(X_test)
+    pred_time = (time.time() - start) / len(X_test)
+    val_loss = log_loss(y_test, y_pred, labels=labels, sample_weight=weight_val)
     y_pred = estimator.predict_proba(X_train)
     train_loss = log_loss(y_train, y_pred, labels=labels, sample_weight=weight_train)
     alpha = 0.5