ISYE 6501 Midterm bundled exams 2023 with 100% correct answers

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 e...

1-norm 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(|

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 inte...

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 f...

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 C...

Descriptive Analytics What happened Predictive Analytics What will happen Prescriptive Analytics What action(s) would be best algorithm Step-by-step procedure designed to carry out a task. change detection Identifying when a significant change has taken place in a pr...

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 Forbidden Factors Things that cannot be used due to legal requirements Exploration Gath...

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 mini...

greedy algorithm at each step, the algorithm does the thing that looks best without taking future options into consideration; more classical variable selection methods stepwise - (forward, backward, combination) lasso elastic net available metrics for variable selection criteria p...

What is modeling? Describing a real life situation mathematically Model Mathematical approaches to solving analytics problems 1. Regression for ex. can be called the model of choice. 2. If more detail (descriptive and numerical), the use of, for ex. Regression, can be called the model. 3...

Matching models/methods to categories (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): k-nearest-neighbor, PCA, k-means, logistic regression, linear regression, random forest, SVM's Classi...

Elastic Net Constrain combination of absolute value of coefficients and their squares. Choose tau and upsilon. Ridge Regression Take out absolute value term from Elastic Net. Doesn't do variable selection, but does lead to better predictive models. LASSO Constraint added to s...