ISYE 6501 Combined

ISYE-6501 Exam 1 QUESTIONS & CORRECT ANSWERS Algorithm - ANSWER a step-by-step procedure designed to carry out a task Change Detection - ANSWER Identifying when a significant change has taken place Classification - ANSWER Separation of data into two or more categories Classifier - ANSWER...

ISYE 6501 - Midterm 2 EXAM QUESTIONS WITH VERIFIED SOLUTIONS 100% LATEST UPDATE When might overfitting occur - ANSWER 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 ...

ISYE 6501 - Final Prep – Midterm Questions With Correct Answers Types of classification models KNN SVM Types of Clustering Models K-Means Clustering Types of Response Prediction Models ARIMA CART Exponential Smoothing Linear Regression Logistic Regression Random Forest Types of Val...

ISYE 6501 Midterm 1 QUESTIONS CORRECTLY ANSWERED LATEST UPDATE Support Vector Machine(SVM) is a supervised machine learning algorithm used for? - ANSWER Classification How to split the data if we only have one model? - ANSWER 70% training data, 30% testing data How to split the data if we w...

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 da...

ISYE6414 (Regression) Midterm 1 QUESTIONS & CORRECT ANSWERS What are the variables in regression? - ANSWER 1. Response (dependent) variable - one particular variable that we are interested in understanding or modelling, such as sales of a particular product. 2. Predicting or Explanatory (in...

ISYE 6501 - Introduction to Analytics Weeks 1 – 7 Combined Frequent Exam Questions Correctly Answered Support Vector Machine (SVM) - ANSWER Supervised learning classification tool and algorithm that seeks a dividing hyperplane for any number of dimensions can be used for regression or ...