Wondering if pruning gives up too much valuable information.
Yes, the model becomes smaller, and supposedly more understandable.
But all those tips being whacked contain at least pairs of variables that ought to be acknowledged.
What are your thoughts on this conundrum?
Thanks!
Nicholas Kormanik
This is true in any modeling, the larger the model (in this case the more branches), the better it will fit (apparently).
But there is also a concept called overfitting, which would usually lead to smaller models, as overfitting is not good and essentially is fitting noise. Those extra branches that should get pruned may in fact be overfitting. How can you tell if something is overfit? Usually, by either crossvalidation, or by fitting the model to a training data set, and then evaluating its performance on validation (and test) data sets
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 25. Read more here about why you should contribute and what is in it for you!
Learn the difference between classical and Bayesian statistical approaches and see a few PROC examples to perform Bayesian analysis in this video.
Find more tutorials on the SAS Users YouTube channel.