ISYE 6501 Lecture Notes ISYE 6501 Midterm 2 with complete solution

# Week 5 Notes Variable Selection what do we do with a lot of factors in our models? variable selection helps us choose the best factors for our models variable selection can work for any factor based model - regression / classification why do we not want a lot of factors in our models? - overfitting: when the number of factors is close or larger than number of data points our model will overfit - overfitting: model captures the random effect of our data instead of the real effects too many factors is the same idea - we will model too much of the random effects in our model with few data points overfitting can cause bad estimates if too many factors our model with be influenced too much by the random effect of that data with few data our model can even fit unrelated variables!