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ISYE 6501 FINAL EXAM NEWEST 2025 COMPLETE 200 QUESTIONS AND CORRECT DETAILED ANSWERS (VER, Exams of Computer Science

Chamberlain College of NursingComputer Science

ISYE 6501 FINAL EXAM NEWEST 2025 COMPLETE 200 QUESTIONS AND CORRECT DETAILED ANSWERS (VERIFIED ANSWERS) |ALREADY GRADED A+ Select the type of problem that lasso regression is best suited for. - Classification and/or prediction from feature data - Clustering - Experimental design - Prediction from time-series data - Variable selection and/or prediction from feature data - ANSWER-- Variable Selection and/or prediction from feature data

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ISYE 6501 Final Exam
ISYE 6501 FINAL EXAM NEWEST 2025 COMPLETE 200
QUESTIONS AND CORRECT DETAILED ANSWERS (VERIFIED
ANSWERS) |ALREADY GRADED A+
Select the type of problem that lasso regression is best suited for.
- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Variable selection and/or prediction from feature data - ANSWER-- Variable Selection and/or
prediction from feature data
Useful when you want to perform variable selection and regularization in linear regression
models, reducing the impact of irrelevant features.
Select the type of problem that support vector machine is best suited for.
- Classification and/or prediction from feature data
- Clustering
- Experimental design
- Prediction from time-series data
- Variable selection - ANSWER-- Classification and/or prediction from feature data
Useful when you want to classify data into different categories and have labeled training data.
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Download ISYE 6501 FINAL EXAM NEWEST 2025 COMPLETE 200 QUESTIONS AND CORRECT DETAILED ANSWERS (VER and more Exams Computer Science in PDF only on Docsity!

ISYE 6501 FINAL EXAM NEWEST 2025 COMPLETE 200

QUESTIONS AND CORRECT DETAILED ANSWERS (VERIFIED

ANSWERS) |ALREADY GRADED A+

Select the type of problem that lasso regression is best suited for.

  • Classification and/or prediction from feature data
  • Clustering
  • Experimental design
  • Prediction from time-series data
  • Variable selection and/or prediction from feature data - ANSWER-- Variable Selection and/or prediction from feature data Useful when you want to perform variable selection and regularization in linear regression models, reducing the impact of irrelevant features. Select the type of problem that support vector machine is best suited for.
  • Classification and/or prediction from feature data
  • Clustering
  • Experimental design
  • Prediction from time-series data
  • Variable selection - ANSWER-- Classification and/or prediction from feature data Useful when you want to classify data into different categories and have labeled training data.

Select the type of problem that k-means is best suited for.

  • Classification and/or prediction from feature data
  • Clustering
  • Experimental design
  • Prediction from time-series data
  • Prediction from feature data
  • Variable selection - ANSWER-- Clustering Useful when you want to cluster data into k distinct groups based on similarity and have unlabeled data. Select the type of problem that GARCH is best suited for.
  • Classification and/or prediction from feature data
  • Clustering
  • Experimental design
  • Prediction from time-series data
  • Prediction from feature data
  • Variable selection - ANSWER-- Prediction from Time-series data Useful when you want to model and forecast the volatility of financial time series data (e.g., stock returns) and have data with time-varying variance. Select the type of problem that exponential smoothing is best suited for.
  • Using feature data to predict the probability of something happening two time periods in the future
  • Using feature data to predict whether or not something will happen two time periods in the future
  • Using time-series data to predict the amount of something two time periods in the future
  • Using time-series data to predict the variance of something two time periods in the future - ANSWER-Using feature data to predict the amount of something two time periods in the future Select the type of analysis that a support vector machine is best suited for.
  • Using feature data to predict the amount of something two time periods in the future
  • Using feature data to predict the probability of something happening two time periods in the future
  • Using feature data to predict whether or not something will happen two time periods in the future
  • Using time-series data to predict the amount of something two time periods in the future
  • Using time-series data to predict the variance of something two time periods in the future - ANSWER-Using feature data to predict whether or not something will happen two time periods in the future Select the type of analysis that a k-nearest-neighbor classification tree is best suited for.
  • Using feature data to predict the amount of something two time periods in the future
  • Using feature data to predict the probability of something happening two time periods in the future
  • Using feature data to predict whether or not something will happen two time periods in the future
  • Using time-series data to predict the amount of something two time periods in the future
  • Using time-series data to predict the variance of something two time periods in the future - ANSWER-most commonly used to predict whether or not something will happen two time periods in the future using feature data. Select the type of problem that Linear Regression is best suited for.
  • Classification
  • Clustering
  • Experimental design
  • Prediction from feature data
  • Prediction from time-series data
  • Variable selection - ANSWER-- Prediction from feature data Useful when you want to model the relationship between a dependent variable and one or more independent variables with a linear assumption. Select the type of problem that ARIMA is best suited for.
  • Classification and/or prediction from feature data
  • Clustering
  • Experimental design
  • Prediction from time-series data
  • Variable selection - ANSWER-- Prediction from Time-series data Useful when you want to forecast future values in a time series (e.g., stock prices, sales) and have historical time-ordered data available.
  • Using feature data to predict the probability of something happening two time periods in the future
  • Using feature data to predict whether or not something will happen two time periods in the future
  • Using time-series data to predict the amount of something two time periods in the future
  • Using time-series data to predict the variance of something two time periods in the future - ANSWER-Using feature data to predict whether or not something will happen two time periods in the future Select the type of analysis that exponential smoothing is best suited for.
  • Using feature data to predict the amount of something two time periods in the future
  • Using feature data to predict the probability of something happening two time periods in the future
  • Using feature data to predict whether or not something will happen two time periods in the future
  • Using time-series data to predict the amount of something two time periods in the future
  • Using time-series data to predict the variance of something two time periods in the future - ANSWER-Using time-series data to predict the amount of something two time periods in the future Select the type of analysis that a logistic regression tree is best suited for.
  • Using feature data to predict the amount of something two time periods in the future
  • Using feature data to predict the probability of something happening two time periods in the future
  • Using feature data to predict whether or not something will happen two time periods in the future
  • Using time-series data to predict the amount of something two time periods in the future
  • Using time-series data to predict the variance of something two time periods in the future - ANSWER-Using feature data to predict the probability of something happening and/or whether or not something will happen two time periods in the future For each type of data, specify whether it is or is not time-series data: i. Number of people who saw any movie on each of the past 1000 days ii. Length of each movie released in the past 1000 days iii. Fraction of tickets sold for animated movies, on each of the past 1000 days iv. Characteristics of a movie (length, leading actor/actress, genre, whether it's animated, etc.) that might affect the number of people who see it - ANSWER-TIME-SERIES NOT TIME-SERIES TIMESERIES NOT TIME-SERIES Below are three statements about data that is scaled before point outliers are removed. For each statement, select the choice that makes the statement correct. i. If data is scaled first, the range of data after outliers are removed will be ________ than intended. ii. Point outliers ____________ appear to be valid data if not removed before scaling. iii. Valid data ____________ appear to be outliers if data is scaled first. - ANSWER-NARROWER

Which type of model is ARENA is best suited for?

  • Discrete-event simulation
  • Linear regression
  • Linear programming (optimization) - ANSWER-Discrete-event simulation Which type of model is PuLP is best suited for?
  • Discrete-event simulation
  • Linear regression
  • Linear programming (optimization) - ANSWER-Linear programming (optimization) Select the analytics task that the R function, GLM, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Support vector machine
  • None of the other choices - ANSWER-Linear regression Select the analytics task that the R function, FrF2, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Support vector machine
  • None of the other choices - ANSWER-None of the other choices Select the analytics task that the R function, train, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Train various models
  • Support vector machine
  • None of the other choices - ANSWER-Support vector machine Select the analytics task that the R function, kknn, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Train various models
  • Support vector machine
  • None of the other choices - ANSWER-k-nearest-neighbor Select the analytics task that the R function, scale, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Train various models
  • Support vector machine
  • None of the other choices - ANSWER-Scale data Select the analytics task that the R function, predict, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Train various models
  • Support vector machine
  • None of the other choices - ANSWER-Random forest Select the analytics task that the R function, prcomp, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Train various models
  • Support vector machine
  • None of the other choices - ANSWER-PCA Select the analytics task that the R function, kmeans, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters
  • k-means
  • k-nearest-neighbor
  • Linear regression
  • Make predictions from models
  • PCA
  • Random forest
  • Scale data
  • Train various models
  • Support vector machine
  • None of the other choices - ANSWER-k-means Select the analytics task that the R function, cv, is directly suitable for.
  • Cross-validation
  • Graphing
  • Holt-Winters

Which of the following three statements is correct?

  • Every model's expected performance on training data will be the same as its expected performance on the validation data, because both the training data and the validation data are taken from the same population.
  • Every model's expected performance on training data will be worse than its expected performance on the validation data, because the training data and the validation data are different.
  • Every model's expected performance on training data will be better than its expected performance on the validation data, because model fits partly to random patterns in the training data. - ANSWER-Every model's expected performance on training data will be better than its expected performance on the validation data, because model fits partly to random patterns in the training data. Which of the following three statements is correct?
  • The selected model's expected performance on test data will be better than its expected performance on the validation data, because there is a selection bias: the selected model is more likely to have worse-than-average performance on random patterns in the validation data.
  • The selected model's expected performance on test data will be the same as its expected performance on the validation data, because the validation data and the test data are the same.
  • The selected model's expected performance on test data will be worse than its expected performance on the validation data, because there is a selection bias: the selected model is more likely to have better-than-average performance on random patterns in the validation data.
  • ANSWER-The selected model's expected performance on test data will be worse than its expected performance on the validation data, because there is a selection bias: the selected

model is more likely to have better-than-average performance on random patterns in the validation data. Which of the following three statements is correct?

  • It is unclear how the selected model's expected performance on test data compares to its observed performance on real-time data, because the training data and the test data were taken from the same population, but the real-time data might be different
  • The selected model's expected performance on test data must be worse than its observed performance on real-time data, because the training data and test data were taken from the same population, but the real-time data might be different.
  • The selected model's expected performance on test data must be better than its observed performance on real-time data, because the training data and test data were taken from the same population, but the real-time data might be different. - ANSWER-It is unclear how the selected model's expected performance on test data compares to its observed performance on real-time data, because the training data and the test data were taken from the same population, but the real-time data might be different A positive correlation has been observed between hours of sleep and self-reported happiness (people who sleep more are happier, and happier people sleep more). Based on that observed correlation, select all of the following statements about the direction of causality between sleep and happiness that are true. A. Lack of sleep makes people unhappy: The less people sleep, the less happy they feel. B. Unhappiness causes lack of sleep: When people feel unhappy, they have trouble sleeping.