ISYE 6501 Midterm 2 Part 1
greedy algorithm - ANS-at each step, the algorithm does the thing that looks best
without taking future options into consideration; more classical
available metrics for variable selection criteria - ANS-p-value
r2
AIC / BIC
lasso - ANS-Giving regression a budget to use on coefficients which it uses on most
important coefficients
Have to scale first
elastic net - ANS-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 - ANS-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 - ANS-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 - ANS-something that could create variance in DOE
A/B Testing - ANS-experiment design to test two alternatives; binomial data used to
answer (with hypothesis test) which is better choice
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