No, the bagged samples are simple random samples.
If the root node of a tree is not split for any reason, that tree is thrown out, a new sample is drawn, and splitting is attempted on that one.
The number of attempted trees is twice the number of requested trees. For example, if 100 trees are requested, then up to 200 samples might be drawn to create a tree.
-Padraic
No, the bagged samples are simple random samples.
If the root node of a tree is not split for any reason, that tree is thrown out, a new sample is drawn, and splitting is attempted on that one.
The number of attempted trees is twice the number of requested trees. For example, if 100 trees are requested, then up to 200 samples might be drawn to create a tree.
-Padraic
My only ideas are:
A. increase the in-bag-fraction to, say, .9 from .6.
B. randomly delete most observations from the dominant target class before running the forest.
A more elaborate B would average the predictions of, say, 10 forests of 10 trees, where each forest is trained with different randomly deleted observations from the dominant target class.
I will use your note to advocate adding stratified sampling in PROC FOREST, the SAS Viya PROC superseding PROC HPFOREST.
-Padraic
Don't miss out on SAS Innovate - Register now for the FREE Livestream!
Can't make it to Vegas? No problem! Watch our general sessions LIVE or on-demand starting April 17th. Hear from SAS execs, best-selling author Adam Grant, Hot Ones host Sean Evans, top tech journalist Kara Swisher, AI expert Cassie Kozyrkov, and the mind-blowing dance crew iLuminate! Plus, get access to over 20 breakout sessions.
Use this tutorial as a handy guide to weigh the pros and cons of these commonly used machine learning algorithms.
Find more tutorials on the SAS Users YouTube channel.