From 8f24bccc653be5d85437858dc383577bcb7c0877 Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Wed, 2 Jun 2021 20:24:06 -0400
Subject: [PATCH] regression 3 quiz
---
Regression/4-Logistic/README.md | 2 -
quiz-app/src/assets/translations/en.json | 64 +++++++++++++-----------
2 files changed, 36 insertions(+), 30 deletions(-)
diff --git a/Regression/4-Logistic/README.md b/Regression/4-Logistic/README.md
index 421d4fb8..377ebbdf 100644
--- a/Regression/4-Logistic/README.md
+++ b/Regression/4-Logistic/README.md
@@ -105,8 +105,6 @@ sns.catplot(x="Color", y="Item Size",
Now that we have an idea of the relationship between the binary categories of color and the larger group of sizes, let's explore Logistic Regression to determine a given pumpkin's likely color.
-> infographic here (an image of logistic regression's sigmoid flow, like this: https://wikipedia.org/wiki/Logistic_regression#/media/File:Exam_pass_logistic_curve.jpeg)
-
> **🧮 Show Me The Math**
>
> Remember how Linear Regression often used ordinary least squares to arrive at a value? Logistic Regression relies on the concept of 'maximum likelihood' using [sigmoid functions](https://wikipedia.org/wiki/Sigmoid_function). A 'Sigmoid Function' on a plot looks like an 'S' shape. It takes a value and maps it to somewhere between 0 and 1. Its curve is also called a 'logistic curve'. Its formula looks like thus:
diff --git a/quiz-app/src/assets/translations/en.json b/quiz-app/src/assets/translations/en.json
index 2d409790..cc629e6e 100644
--- a/quiz-app/src/assets/translations/en.json
+++ b/quiz-app/src/assets/translations/en.json
@@ -669,48 +669,52 @@
"title": "Logistic Regression: Pre-Lecture Quiz",
"quiz": [
{
- "questionText": "q1",
+ "questionText": "Use Logistic Regression to predict",
"answerOptions": [
{
- "answerText": "a",
- "isCorrect": "false"
+ "answerText": "whether an apple is ripe or not",
+ "isCorrect": "true"
},
{
- "answerText": "b",
- "isCorrect": "true"
+ "answerText": "how many tickets can be sold in a month",
+ "isCorrect": "false"
},
{
- "answerText": "c",
+ "answerText": "what color the sky will turn tomorrow at 6 PM",
"isCorrect": "false"
}
]
},
{
- "questionText": "q2",
+ "questionText": "Types of Logistic Regression include",
"answerOptions": [
{
- "answerText": "a",
+ "answerText": "multinomial and cardinal",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "multinomial and ordinal",
"isCorrect": "true"
},
{
- "answerText": "b",
+ "answerText": "principal and ordinal",
"isCorrect": "false"
}
]
},
{
- "questionText": "q3",
+ "questionText": "Your data has weak correlations. The best type of Regression to use is:",
"answerOptions": [
{
- "answerText": "a",
- "isCorrect": "false"
+ "answerText": "Logistic",
+ "isCorrect": "true"
},
{
- "answerText": "b",
- "isCorrect": "true"
+ "answerText": "Linear",
+ "isCorrect": "false"
},
{
- "answerText": "c",
+ "answerText": "Cardinal",
"isCorrect": "false"
}
]
@@ -722,48 +726,52 @@
"title": "Logistic Regression: Post-Lecture Quiz",
"quiz": [
{
- "questionText": "q1",
+ "questionText": "Seaborn is a type of",
"answerOptions": [
{
- "answerText": "a",
- "isCorrect": "false"
+ "answerText": "data visualization library",
+ "isCorrect": "true"
},
{
- "answerText": "b",
- "isCorrect": "true"
+ "answerText": "mapping library",
+ "isCorrect": "false"
},
{
- "answerText": "c",
+ "answerText": "mathematical library",
"isCorrect": "false"
}
]
},
{
- "questionText": "q2",
+ "questionText": "A confusion matrix is also known as a:",
"answerOptions": [
{
- "answerText": "a",
+ "answerText": "error matrix",
"isCorrect": "true"
},
{
- "answerText": "b",
+ "answerText": "truth matrix",
+ "isCorrect": "false"
+ },
+ {
+ "answerText": "accuracy matrix",
"isCorrect": "false"
}
]
},
{
- "questionText": "q3",
+ "questionText": "A good model will have:",
"answerOptions": [
{
- "answerText": "a",
+ "answerText": "a large number of false positives and true negatives in its confusion matrix",
"isCorrect": "false"
},
{
- "answerText": "b",
+ "answerText": "a large number of true positives and true negatives in its confusion matrix",
"isCorrect": "true"
},
{
- "answerText": "c",
+ "answerText": "a large number of true positives and false negatives in its confusion matrix",
"isCorrect": "false"
}
]
--
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