Midterm Quiz 2

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University of California, Berkeley *

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6501

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Computer Science

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May 27, 2024

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Course Midterm Quiz 2 - Summer 2020 Midterm Quiz 2 Midterm Quiz 2 - GT Students and Veri±ed MM Learners Midterm Quiz 2 - GT Students and Veri±ed MM Learners Midterm Quiz 2 due Jul 7, 2020 23:00 PDT Past Due 90 Minute Time Limit Instructions Work alone. Do not collaborate with or copy from anyone else. You may use any of the following resources: One sheet (both sides) of handwritten (not photocopied or scanned) notes If any question seems ambiguous, use the most reasonable interpretation (i.e. don't be like Calvin):
Good Luck! This is the beginning of Midterm Quiz 2. Please make sure that you submit all your answers before the time runs out. Once you submit an answer to a question, you cannot change it. There is no overall Submit button. After submitting all answers, please click the "End my Exam" button, above, before exiting from ProctorTrack to complete your exam. Information for Question 1 There are ±ve questions labeled "Question 1." Answer all ±ve questions. For each of the following ±ve questions, select the probability distribution that could best be used to model the described scenario. Each distribution might be used, zero, one, or more than one time in the ±ve questions. These scenarios are meant to be simple and straightforward; if you're an expert in the ±eld the question asks about, please do not rely on your expertise to ±ll in all the extra complexity (you'll end up making the questions below more di²cult than I intended).
Question 1 1.4/1.4 points (graded) Number of hits to a real estate web site each minute Poisson You have used 1 of 1 attempt Question 1 1.4/1.4 points (graded) Time between hits on a real estate web site Exponential You have used 1 of 1 attempt Question 1 1.4/1.4 points (graded) Time from when a house is put on the market until the ±rst o²er is received Weibull You have used 1 of 1 attempt Question 1 1.4/1.4 points (graded) Time between people entering the ID-check queue at an airport Submit Submit Submit
Exponential You have used 1 of 1 attempt Question 1 1.4/1.4 points (graded) Number of faces correctly identi±ed by deep learning (DL) software until an error is made Geometric You have used 1 of 1 attempt Questions 2a, 2b 10.0/10.0 points (graded) Five classi±cation models were built for predicting whether a neighborhood will soon see a large rise in home prices, based on public elementary school ratings and other factors. The training data set was missing the school rating variable for every new school (3% of the data points). Because ratings are unavailable for newly-opened schools, it is believed that locations that have recently experienced high population growth are more likely to have missing school rating data. Model 1 used imputation, ±lling in the missing data with the average school rating from the rest of the data. Model 2 used imputation, building a regression model to ±ll in the missing school rating data based on other variables. Model 3 used imputation, ±rst building a classi±cation model to estimate (based on other variables) whether a new school is likely to have been built as a result of recent population growth (or whether it has been built for another purpose, e.g. to replace a very old school), and then using that classi±cation to select one of two regression models to ±ll in an estimate of the school rating; there are Submit Submit
two di±erent regression models (based on other variables), one for neighborhoods with new schools built due to population growth, and one for neighborhoods with new schools built for other reasons. Model 4 used a binary variable to identify locations with missing information. Model 5 used a categorical variable: ²rst, a classi²cation model was used to estimate whether a new school is likely to have been built as a result of recent population growth; and then each neighborhood was categorized as "data available", "missing, population growth", or "missing, other reason". a. If school ratings can be reasonably well-predicted from the other factors, and new schools built due to recent population growth can be reasonably well-classi²ed using the other factors, which model would you recommend? b. In which of the following situations would you recommend using Model 2? [All predictions and classi²cations below are using the other factors.] Model 1 Model 2 Model 3 Model 4 Model 5 Ratings can be well-predicted, and reasons for building schools can be well-classi²ed. Ratings can be well-predicted, and reasons for building schools cannot be well-classi²ed.
You have used 1 of 1 attempt Information for Question 3 In a diet problem (like we saw in the lessons and homework), let x be the amount of food i in the solution ( x >= 0) , and let M be the maximum amount that can be eaten of any food. Suppose we added new variables y that are binary (i.e., they must be either 0 or 1): if food i is eaten in the solution, then it is part of the solution ( y = 1) ; otherwise y = 0 . There are ±ve questions labeled "Question 3." Answer all ±ve questions. For each of the following ±ve questions, select the mathematical constraint that best corresponds to the English sentence. Each constraint might be used, zero, one, or more than one time in the ±ve questions. Question 3 1.4/1.4 points (graded) Select the mathematical constraint that corresponds to the following English sentence: Ratings cannot be well-predicted, and reasons for building schools can be well-classi±ed. Ratings cannot be well-predicted, and reasons for building schools cannot be well-classi±ed. Submit i i i i i
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