From 96eeed81944118dae79a6b0c122bf29b3386ac1b Mon Sep 17 00:00:00 2001
From: udlbook <110402648+udlbook@users.noreply.github.com>
Date: Tue, 14 Nov 2023 08:58:30 +0000
Subject: [PATCH] Created using Colaboratory

---
 Notebooks/Chap21/21_1_Bias_Mitigation.ipynb | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb b/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb
index 00f144d..9962b3d 100644
--- a/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb
+++ b/Notebooks/Chap21/21_1_Bias_Mitigation.ipynb
@@ -4,7 +4,7 @@
   "metadata": {
     "colab": {
       "provenance": [],
-      "authorship_tag": "ABX9TyPz0X4nKJUCBhv5l4Z/xUVo",
+      "authorship_tag": "ABX9TyNQPfTDV6PFG7Ctcl+XVNlz",
       "include_colab_link": true
     },
     "kernelspec": {
@@ -59,7 +59,7 @@
         "# Worked example: loans\n",
         "\n",
         "Consider the example of an algorithm $c=\\mbox{f}[\\mathbf{x},\\boldsymbol\\phi]$ that predicts credit rating scores $c$ for loan decisions.  There are two pools of loan applicants identified by the variable $p\\in\\{0,1\\}$ that we’ll describe as the blue and yellow populations. We assume that we are given historical data, so we know both the credit rating and whether the applicant actually defaulted on the loan ($y=0$) or\n",
-        " repaid it ($y=1).\n",
+        " repaid it ($y=1$).\n",
         "\n",
         "We can now think of four groups of data corresponding to (i) the blue and yellow populations and (ii) whether they did or did not repay the loan. For each of these four groups we have a distribution of credit ratings (figure 1). In an ideal world, the two distributions for the yellow population would be exactly the same as those for the blue population. However, as figure 1 shows, this is clearly not the case here."
       ],