regression 3 quiz

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:

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"
 							}
 						]