ISYE 6501 Spring '23 Exam 1 QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024

ISYE 6501 Spring '23 Exam 1 QUESTIONS WITH 100% SOLUTIONS LATEST UPDATE 2023/2024 What is modeling? - ANSWER Describing a real-life situation mathematically, analyzing the math and then turning the math back into a real-life situation. What is a data point? - ANSWER A row of data. All of the information about one observation. What are some (4) names for columns of data? - ANSWER Attributes, features, covariates, predictors Name some common types of structured data (5) - ANSWER Quantitative, categorical, binary, unrelated, time series What is binary data? - ANSWER A subset of categorical data (although can be treated as numerical) that can only take on one value (Y/N, M/F, etc.) What is unstructured data? Example? - ANSWER Data that is not easily described or stored. Ex: Text. When do you use a soft classifier? - ANSWER When you can't draw a line to divide all data points. What are support vectors? - ANSWER Points supporting a shape on parallel lines. SVM - what does it mean if the coefficients are near zero? - ANSWER Those coefficients are probably not relevant for classification. SVM - Does a classifier need to be a straight line? - ANSWER No. Kernel methods allow for nonlinear classifiers. What is the most common scaling used? - ANSWER Scaling data between 0 and 1. What is standardization? - ANSWER Scaling data to a normal distribution (typically mean = 0, sd = 1) When might you use scaling over standardization? - ANSWER When your data needs to be in a bounded range. Ex: neural networks, optimization models, etc. What are two models that work better with standardization over scaling? - ANSWER Principle component analysis and clustering. How does KNN determine what a new point's class will be? - ANSWER The new data point's class is the most common class among the k neighbors What is the most common method to measure distance between k nearest neighbors? - ANSWER A straight line, although other methods can be used How can you adjust KNN for attributes that are more important for the classification? - ANSWER Weight the distances and give those attributes more weight What type of effects explain why we can't measure the model's effectiveness on training data? - ANSWER Random effects Why is the observed performance on high performing models probably too optimistic? - ANSWER High performing models are most likely to have benefited from random effects. Which set of data is used to choose the best model - ANSWER Validation set Which set of data is used for building models? - ANSWER Training set Which set of data is used for measuring a model's performance? - ANSWER Test set