Linear Regression

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School

Arizona State University *

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Course

598DL

Subject

Statistics

Date

Jun 18, 2024

Type

PNG

Pages

1

Uploaded by DoctorMosquito2332

1. Which of the following scenarios can be viewed as a classification problem ? 1/1 point O Predicting the age of a person given theirimage. O Use the time and weather data to predict the number of bikes being rented at any given hour. O Use the area and age of the house to predict the house price in a given street. @ Use the length and width of the flower petal to predict the species of a flower. @ Correct This should be selected because the output is the species (categorical) given input features length and width. 2. Which of the following scenarios is a regression problem? 1/1 point O Predict whether the house prices will increase in the next quarter. O Predict whether or not a customer will purchase a particular item. @ Predicting the number of books that will be sold in the following year based on sales parameters in the current year. O Predict the sports team given an image of the jersey (team shirt). @ Correct This should be selected because the number of books sold can be a whole humber greater than 0. 3. Which of the following problems is NOT a supervised learning problem? 1/1 point [J Given a collection of 5 medical records of patients with cancer and 1000 records without cancer, predict the odds of a new patient having the cancer given their medical record. [ Given 40 books written by male authors and 60 books written by female authors, predicting the gender of the author given a new book Given a collection of medical records with diabetes, examine them to discover if there are subtypes of diabetes. @ Correct This should be selected because it is an unsupervised setting (particularly clustering), we have only collection of records without any labels of subtypes of diabetes. [J Given a huge database of English sentences and their Spanish translations, translating a new sentence in English to Spanish. 4. Find the total number of parameters in the hypothesis function hg(x) = 8y + 6121 + Gz 1/1 point ® 3parameters, {6y, 01,05} QO 6parameters {6y, 61, 62, 1, z2, h(.)} QO 3parameters {z1, z2, h(.)} (O 1parameter 6 @ Correct Only B's are the parameters.
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