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ISYE 6501 Bundled Exams Questions and Answers (2023/2024) (Complete and Accurate)
ISYE 6501 Bundled Exams Questions and Answers (2023/2024) (Complete and Accurate)
[Show more]ISYE 6501 Bundled Exams Questions and Answers (2023/2024) (Complete and Accurate)
[Show more]ISYE 6501 - Midterm 1 Questions and Answers 100% Pass 
What do descriptive questions ask? What happened? (e.g., which customers are most alike) 
What do predictive questions ask? What will happen? (e.g., what will Google's stock price be?) 
What do prescriptive questions ask? What action(s) would b...
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Add to cartISYE 6501 - Midterm 1 Questions and Answers 100% Pass 
What do descriptive questions ask? What happened? (e.g., which customers are most alike) 
What do predictive questions ask? What will happen? (e.g., what will Google's stock price be?) 
What do prescriptive questions ask? What action(s) would b...
ISYE 6501 Midterm 1 (2023/2024) Already Graded A Rows Data points are values in data tables 
Columns The 'answer' for each data point (response/outcome) 
Structured Data Quantitative, Categorical, Binary, Unrelated, Time Series 
Unstructured Data Text 
Support Vector Model Supervised machine learn...
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Add to cartISYE 6501 Midterm 1 (2023/2024) Already Graded A Rows Data points are values in data tables 
Columns The 'answer' for each data point (response/outcome) 
Structured Data Quantitative, Categorical, Binary, Unrelated, Time Series 
Unstructured Data Text 
Support Vector Model Supervised machine learn...
ISYE 6501 - Midterm 2 Questions and Answers 100% Correct 
when might overfitting occur when the # of factors is close to or larger than the # of data points causing the model to potentially fit too closely to random effects 
Why are simple models better than complex ones less data is required; less ...
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Add to cartISYE 6501 - Midterm 2 Questions and Answers 100% Correct 
when might overfitting occur when the # of factors is close to or larger than the # of data points causing the model to potentially fit too closely to random effects 
Why are simple models better than complex ones less data is required; less ...
ISYE 6501 Final Exam Questions and Answers 100% Pass 
Factor Based Models classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model 
Why limit number of factors in a model? 2 reasons overfitting: when # of factors is close to or larger than # of data...
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Add to cartISYE 6501 Final Exam Questions and Answers 100% Pass 
Factor Based Models classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model 
Why limit number of factors in a model? 2 reasons overfitting: when # of factors is close to or larger than # of data...
ISYE 6501 Midterm Questions and Answers Already Graded A What does SVM stand for? Support Vector Machine 
Is written text structured or unstructured? Unstructured 
When we increase the sum of the square of the coefficients we... Decrease the distance between the lines 
In SVM soft classifier we trad...
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Add to cartISYE 6501 Midterm Questions and Answers Already Graded A What does SVM stand for? Support Vector Machine 
Is written text structured or unstructured? Unstructured 
When we increase the sum of the square of the coefficients we... Decrease the distance between the lines 
In SVM soft classifier we trad...
ISyE 555 Exam 1 Questions and Answers Already Passed What are the two different approaches to safety and how do they differ? - Proactive approach: proactive risk assessment, safety audit, near miss reporting systems - Reactive approaches: accident and incident investigation 
What are the goals of an...
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Add to cartISyE 555 Exam 1 Questions and Answers Already Passed What are the two different approaches to safety and how do they differ? - Proactive approach: proactive risk assessment, safety audit, near miss reporting systems - Reactive approaches: accident and incident investigation 
What are the goals of an...
ISYE 6501 Final Questions and Answers Already Passed Support Vector Machine A supervised learning, classification model. Uses extremes, or identified points in the data from which margin vectors are placed against. The hyperplane between these vectors is the classifier 
SVM Pros/Cons Pros: It works ...
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Add to cartISYE 6501 Final Questions and Answers Already Passed Support Vector Machine A supervised learning, classification model. Uses extremes, or identified points in the data from which margin vectors are placed against. The hyperplane between these vectors is the classifier 
SVM Pros/Cons Pros: It works ...
ISyE 6501 Final Exam Latest 2023 Rated A 1-norm Similar to rectilinear distance; measures the straight-line length of a vector from the origin. If z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-norm is square root(|
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Add to cartISyE 6501 Final Exam Latest 2023 Rated A 1-norm Similar to rectilinear distance; measures the straight-line length of a vector from the origin. If z=(z1,z2,...,zm) is a vector in an m-dimensional space, then it's 1-norm is square root(|
ISYE6501: MIDTERM 1 LATEST 2023 RATED A 
Matching models/methods to categories (cusum and pca = NONE) 
Select all of the following models that are designed for use with attribute/feature data (i.e., not time-series data): k-nearest-neighbor, PCA, k-means, logistic regression, linear regression, rand...
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Add to cartISYE6501: MIDTERM 1 LATEST 2023 RATED A 
Matching models/methods to categories (cusum and pca = NONE) 
Select all of the following models that are designed for use with attribute/feature data (i.e., not time-series data): k-nearest-neighbor, PCA, k-means, logistic regression, linear regression, rand...
ISYE 6501 Midterm 2 Part 1 Latest 2023 Rated A greedy algorithm at each step, the algorithm does the thing that looks best without taking future options into consideration; more classical 
variable selection methods stepwise - (forward, backward, combination) lasso elastic net 
available metrics for...
Preview 2 out of 7 pages
Add to cartISYE 6501 Midterm 2 Part 1 Latest 2023 Rated A greedy algorithm at each step, the algorithm does the thing that looks best without taking future options into consideration; more classical 
variable selection methods stepwise - (forward, backward, combination) lasso elastic net 
available metrics for...
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