Isye 6501 midterm exam 1 Study guides, Class notes & Summaries

ISYE 6501 Midterm 1 Glossary Exam Questions & Answers

ISYE 6501 Midterm 1 EXAM QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024 True or false: In a regression tree, every leaf of the tree has a different regression model that might use different attributes, have different coefficients, etc. - ANSWER True - Each leaf's individual model is tailored to the subset of data points that follow all of the branches leading to the leaf. True or false: Tree-based approaches can be used for other models besides regression. - ANSWER True ...

ISYE-6501 Midterm Exam QUESTIONS & CORRECT ANSWERS What is modeling? A real life situation expressed as math. What is a classification model? A model that puts things into categories. e.g. a loan applicant classification with a SVM splitting people into 2 buckets - yes and no. What is a data point? A 'row' of data essentially. It is not just one attribute (column) on a row of data. What is Quantitative Data numbers that have meaning What is Categorical Data Non-numeric informa...

ISYE 6501 MIDTERM 1 EXAM TEST What do descriptive questions ask? - CORRECT ANSWER-What happened? (e.g., which customers are most alike) What do predictive questions ask? - CORRECT ANSWER-What will happen? (e.g., what will Google's stock price be?) What do prescriptive questions ask? - CORRECT ANSWER-What action(s) would be best? (e.g., where to put traffic lights) What is a model? - CORRECT ANSWER-Real-life situation expressed as math. What do classifiers help you do? - CORRECT ANSWE...

ISYE 6501 Midterm 1 cheat sheet Midterm exam with all the required sections

ISYE 6501 Midterm 1 EXAM QUESTIONS WITH VERIFIED SOLUTIONS 100% LATEST UPDATE 2023/2024 True or false: In a regression tree, every leaf of the tree has a different regression model that might use different attributes, have different coefficients, etc. - ANSWER True - Each leaf's individual model is tailored to the subset of data points that follow all of the branches leading to the leaf. True or false: Tree-based approaches can be used for other models besides regression. - ANSW...

ISYE 6501 MIDTERM EXAM GRADED A+ LATEST What does SVM stand for? -CORRECT ANSWER-Support Vector Machine Is written text structured or unstructured? -CORRECT ANSWER-Unstructured When we increase the sum of the square of the coefficients we... -CORRECT ANSWER-Decrease the distance between the lines In SVM soft classifier we tradeoff between maximizing ___ and minimizing ___ - CORRECT ANSWER-margin and errors If lambda gets small what gets emphasized, large margin or minimizing training e...

ISYE 6501 Midterm EXAM QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024 What does SVM stand for? - ANSWER Support Vector Machine Is written text structured or unstructured? - ANSWER Unstructured When we increase the sum of the square of the coefficients we... - ANSWER Decrease the distance between the lines In SVM soft classifier we tradeoff between maximizing ___ and minimizing ___ - ANSWER margin and errors If lambda gets small what gets emphasized, large margin or minimiz...

ISYE 6501 MIDTERM 1 VERIFIED EXAM TEST Matching models/methods to categories - CORRECT ANSWER-(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): - CORRECT ANSWER-k-nearest-neighbor, PCA, k-means, logistic regression, linear regression, random forest, SVM's Classification models - CORRECT ANSWER-CART, k-nearest-neighbor, logistic regression, random forest, support vector machine Clustering - ...

ISYE 6501 Midterm EXAM QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024 What does SVM stand for? - ANSWER Support Vector Machine Is written text structured or unstructured? - ANSWER Unstructured When we increase the sum of the square of the coefficients we... - ANSWER Decrease the distance between the lines In SVM soft classifier we tradeoff between maximizing ___ and minimizing ___ - ANSWER margin and errors If lambda gets small what gets emphasized, large margin or minimiz...