Isye 6501 Study guides, Class notes & Summaries

1-norm Answer - 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(|

ISYE 6501 - MIDTERM 1 Q&A 2024

ISYE 6501 Midterm Exam 1 Questions and Correct Answers

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 learning algorithm used for both classification and regression challenges. Mostly used in classification problems by plotting each data item as a point in n-dimensional space (n is the number of features y...

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 be best? (e.g., where to put traffic lights) What is a model? Real-life situation expressed as math. What do classifiers help you do? differentiate What is a soft classifier and when is it used? In ...

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 tradeoff between maximizing ___ and minimizing ___ margin and errors If lambda gets small what gets emphasized, large margin or minimizing training error?, Minimizing errors. What is a support vector? A...

ISYE 6501 Midterm Quiz 2 - GT Students and Verified MM Learners 2024 With Complete Solution Midterm Quiz 2 - GT Students and Verified MM Learners

ISYE 6501 FINAL EXAM WITH COMPLETE SOLUTION 2022/2023 1. Factor Based Models: classification, clustering, regression. Implicitly assumed that we have a lot of factors in the final model 2. Why limit number of factors in a model? 2 reasons: overfitting: when # of factors is close to or larger than # of data points. Model may fit too closely to random effects simplicity: simple models are usually better 3. Classical variable selection approaches: 1. Forward selection 2. Backwards eli...

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 chance of insignificant factors and easier to interpret what is forward selection we select the best new factor and see if it's good enough (R^2, AIC, or p-value) add it to our model and fit the mod...