ISYE 6501 Midterm 2 Part 1|2023 LATEST UPDATE|WITH 100% CORRECT ANSWERS|100% PASS

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-value r2 AIC / BIC lasso Giving regression a budget to use on coefficients which it uses on most important coefficients Have to scale first elastic net constrain combination of absolute value of coefficients and their squares have to scale first Note: if absolute value is removed, ridge regression (to be covered later) prediction error function of bias and variance model complexity on x axis error on y axis bias squared decreasing, variance increasing, prediction error concave curve with inflection point where (ish) bias squared = variance design of experiment used as a means to quickly and efficiently get a subset of data Ex: polls, which similar products a retailer should display -keep in mind comparison / control and blocking blocking factor something that could create variance in DOE A/B Testing experiment design to test two alternatives; binomial data used to answer (with hypothesis test) which is better choice Experiment can be run n times or until results are significant A/B testing requirements Quick collection of a lot of data Data must be representative Data collection size must be small compared to total size of use case A/B testing limitations Does not address: Several alternatives, Learning as you go, Combination of factors factorial design experiment design with more than two alternatives, but a small enough set of scenarios to test all testing combinations ANOVA determines importance of each factor fractional factorial design experiment design with too many possible scenarios to test comprehensively where a subset of scenarios is tested independent factorial design experiment design where factors are assumed to be independent; a subset of combinations of choices is tested, and regression is used to estimate effect of each choice; each factor gets a categorical variable Note that interaction terms are likely necessary; ex: font color and background color exploration v exploitation more information v immediate value -- with regards to DOE multi armed bandit approach testing k alternatives & starting with no information; run test and update information about probabilities of each being the best, assign new test Exploration, but since more likely to pick the best one, also exploitation