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# Q7 Quiz Instruction

📗 The quizzes must be completed during the lectures. No Canvas submissions are required. The grades will be updated by the end of the week on Canvas. Alternative ways to get the points are listed below in the D7 sections.
📗 Please submit a regrade request if (i) you missed a few questions because you are late or have to leave during the lecture; (ii) you selected obviously incorrect answers by mistake (one or two of these shouldn't affect your grade): Link

Answer Points Out of
Correct 1 Number of Questions
Plausible but Incorrect 1 -
Obviously Incorrect 0 -



# D2 Sharing Solutions on Piazza

📗 Use the sign-up sheet: Google Sheet. You can sign up and post anonymously (anonymous Piazza posts are not anonymous to instructors).

Requirement M Questions Past Exam Questions
List of Questions M4-5Q1-10 X1Q4-15, X2Q1-6
Due Date Jul 11
Name of Post M?Q? X?Q?
Piazza Tag one of m4-5 or d2 one of m4-5 or d2
Type of Post public Piazza note same
Include (1) Screenshot of the question same
- (2) Detailed step-by-step solution same
- (3) Brief explanation of each step same


If your post satisfies the requirements (1), (2), (3), one of the course staff will "like" your post and you will receive 0.5 points for the post.
The past exams can be found on pages X1, X2, X3, X4, X5,

# D2 Discussion Topic

📗 Please create a follow-up discussion post on the Piazza (it is okay to post anonymously). No Canvas submissions are required. The grades will be updated at the end of the week on Canvas.

Requirement Discussion -
Topic see below -
Due Date Jul 11 -
Type of Post Piazza follow-up -
Include (1) Image or screenshot -
- (2) Brief discussion or explanation (one or two sentences)


If your post satsifies the requirements (1), (2), you will receive 0.5 points for the post.

📗 Find or draw a decision tree. The decision tree can be created based on a dataset or a flow chart for a task or just a meme. Share an image on Piazza, and explain whether it is a regression tree, a classification tree, or neither. Also write down the output variable and list some of the input features.
📗 A regression tree is a decision tree that has continuous-valued labels, i.e. \(y \in \mathbb{R}\). A classification tree has discrete-valued labels, i.e. \(y \in \left\{1, 2, ...\right\}\).
📗 A tree that is neither a regression tree or a classification tree can be one describing a extensive form game (single-person or multiple-players). Technically, these are not decision trees and we will talk about game theory near the end of the semester, for now, you can read its Wikipedia page: Link.

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Last Updated: November 18, 2024 at 11:43 PM