From 407772c2fceefda8ec7d818bf2c4d65a5abde475 Mon Sep 17 00:00:00 2001
From: Richard Stotz <richardstotz@google.com>
Date: Mon, 3 Jul 2023 07:00:15 -0700
Subject: [PATCH] Fixed a typo in the uplift tutorial and clarified AUUC

PiperOrigin-RevId: 545212831
---
 documentation/tutorials/uplift_colab.ipynb | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/documentation/tutorials/uplift_colab.ipynb b/documentation/tutorials/uplift_colab.ipynb
index 2884797..0140d69 100644
--- a/documentation/tutorials/uplift_colab.ipynb
+++ b/documentation/tutorials/uplift_colab.ipynb
@@ -67,7 +67,7 @@
         "\n",
         "In this colab, you will:\n",
         "\n",
-        "- Learn what an uplift modeling is is.\n",
+        "- Learn what an uplift modeling is.\n",
         "- Train a Uplift Random Forest model on the **Hillstrom Email Marketing** dataset.\n",
         "- Evaluate the quality of this model.\n"
       ]
@@ -177,7 +177,7 @@
       "source": [
         "## What is uplift modeling?\n",
         "\n",
-        "[Uplift modeling](https://en.wikipedia.org/wiki/Uplift_modeling) is a statistical modeling technique to predict the **incremental impact of an action** on a subject. The action is often referred to as a **treatment** that may or may not be applied.\n",
+        "[Uplift modeling](https://en.wikipedia.org/wiki/Uplift_modelling) is a statistical modeling technique to predict the **incremental impact of an action** on a subject. The action is often referred to as a **treatment** that may or may not be applied.\n",
         "\n",
         "Uplift modeling is often used in targeted marketing campaigns to predict the increase in the likelihood of a person making a purchase (or any other desired action) based on the marketing exposition they receive.\n",
         "\n",
@@ -382,11 +382,11 @@
         "\n",
         "Suppose you have a labeled dataset with $|T|$ examples with treatment and $|C|$ examples without treatment, called *control* examples. For each example, the uplift model $f$ produces the conditional probability that a treatment on the example will yield a positive outcome.\n",
         "\n",
-        "Suppose a decision-maker needs to decide which clients to send a voucher using an uplift model $f$. The model produces a (conditional) probability that the voucher will result in a conversion. The decision-maker might therefore just pick the number $k$ of vouchers to send and send those $k$ vouchers to the clients with the highest probability.\n",
+        "Suppose a decision-maker needs to decide which clients to send an email using an uplift model $f$. The model produces a (conditional) probability that the email will result in a conversion. The decision-maker might therefore just pick the number $k$ of emails to send and send those $k$ emails to the clients with the highest probability.\n",
         "\n",
-        "Using a labeled test dataset, it is possible to study the impact of $k$ on the success of the campaign. First, we are interested in the ratio $\\frac{|C \\cap T|}{|T|}$ of clients with voucher that converted versus total number of clients with voucher. Here $C$ is the set of clients that converted and $T$ is the number of clients that coverted. We plot this ratio against $k$.\n",
+        "Using a labeled test dataset, it is possible to study the impact of $k$ on the success of the campaign. First, we are interested in the ratio $\\frac{|C \\cap T|}{|T|}$ of clients that received an email that converted versus total number of clients that received an email. Here $C$ is the set of clients that received an email and converted and $T$ is the total number of clients that received an email. We plot this ratio against $k$.\n",
         "\n",
-        "Ideally, we like to have this curve increase steeply. This would mean that the model prioritizes sending vouchers to those clients that will generate a conversion when receiving a voucher."
+        "Ideally, we like to have this curve increase steeply. This would mean that the model prioritizes sending email to those clients that will generate a conversion when receiving an email."
       ]
     },
     {
@@ -429,7 +429,7 @@
         "id": "97IFpq5epHsx"
       },
       "source": [
-        "Similarly, we can also compute and plot the conversion ratio of those not receiving a voucher, called the *control group*. Ideally, this curve is initially flat: This would mean that the model does not prioritize sending vouchers to clients that will generate a conversion despite **not** receiving a voucher"
+        "Similarly, we can also compute and plot the conversion ratio of those not receiving an email, called the *control group*. Ideally, this curve is initially flat: This would mean that the model does not prioritize sending emails to clients that will generate a conversion despite **not** receiving a email"
       ]
     },
     {