ISYE 6501

ISYE 6501 Midterm 2. Top Questions and answers, 100% Accurate, rated A+ Variable Selection - - When two predictors are highly correlated, which of the following statements is true? a) Lasso regression will usually have non-zero coefficients for both predictors. b) Ridge regression will usually have non-zero coefficients for both predictors. - -b) Ridge regression will usually have non-zero coefficients for both predictors. Correct: Ridge regression will choose smaller (in an absolu...

ISYE-6501 Exam 1 Top Questions and answers, 100% Accurate. Rated A+ Algorithm - -a step-by-step procedure designed to carry out a task Change Detection - -Identifying when a significant change has taken place Classification - -Separation of data into two or more categories Classifier - -A boundary that separates data into two or more categories Cluster - -A group of points that are identified as being similar or near each other Cluster Center - -In some clustering algorithms ...

ISYE 6501 -Exam 2 Wks 8 – 12. Top Exam Questions and answers, 100% Accurate. Building simpler models with fewer factors helps avoid which problems? A. Overfitting B. Low prediction quality C. Bias in the most important factors D. Difficulty in interpretation - -A. Overfitting D. Difficulty of interpretation Two main reasons to limit # of factors in a model. - -1. Overfitting 2. Simplicity When is overfitting likely to happen? - -When the number of factors is close to the numbe...

ISYE 6501 - Midterm 2, Top Exam Questions and answers, 100% Accurate. VERIFIED. 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 i...

ISYE 6501 Questions Exam 2. 100% Accurate. Graded A+ Overfitting If you have less data than features, what is likely to occur? Fitting random effects What can too many factors lead to? Simple Models Reducing variables will result in

ISYE 6501 Midterm Exam Questions & Answers, 100% Accurate. RATED 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

ISYE 6501 - Midterm 1. Top Exam Questions & Answers. Rated A+ 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 sof...

ISYE 6501 Final Exam Questions and answers, 100% Accurate. 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 points. Model may fit too closely to random effects simplicity: simple models are usually better Classical variable selection approaches 1. Forward selection 2. Backward...

ISYE 6501 - Midterm 2 Exam Questions and answers, 100% Accurate. Rated A+ 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...

ISYE 6501 Final Exam Questions and answers, 100% ACCURATE. RATED A+ 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 points. Model may fit too closely to random effects simplicity: simple models are usually better Classical variable selection approaches 1. Forward selection 2....