From f50a96ddbe4be9b8ac1dc27357f09e018a64fcf7 Mon Sep 17 00:00:00 2001
From: Zeynep Hakguder <zhakguder@cse.unl.edu>
Date: Fri, 1 Jun 2018 15:39:35 -0500
Subject: [PATCH] pa1
---
ProgrammingAssignment_1/ProgrammingAssignment1.ipynb | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
index bb66aac..78a6586 100644
--- a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
+++ b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
@@ -138,7 +138,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## TASK 2: Implement $k$-NN Class Methods"
+ "## TASK 2: Implement *k*-NN Class Methods"
]
},
{
@@ -423,7 +423,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "ROC curves are a good way to visualize sensitivity vs. 1-specificity for varying cut off points. Now, implement, in *model.ipynb*, a \"ROC\" function. \"ROC\" takes a list containing different threshold values to try and returns two arrays; one where each entry is the sensitivity at a given threshold and the other where entries are 1-specificities."
+ "ROC curves are a good way to visualize sensitivity vs. 1-specificity for varying cut off points. Now, implement, in **model.ipynb**, a \"ROC\" function. \"ROC\" takes a list containing different threshold values to try and returns two arrays; one where each entry is the sensitivity at a given threshold and the other where entries are 1-specificities."
]
},
{
--
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