Interesting -- if you've got a link, please post it.
So the extrapolation-type problem you describe (an input not near any of your training examples) is an issue. Unless you have a world model you believe in (i.e. you've done some science -- not just statistics), hard to…
I imagine Breiman was just talking about bagging-style parallel ensembles, when he was talking about variance reduction, not boosting-style sequential ensembles. Not long before he died, he was still actively trying to…
Hehe ok —- I also love Breiman’s Probability book. It’s really a standout on Ergodic theory. And Breiman et al.’s book on Trees is surprisingly rich, talking about all sorts of stuff besides trees.
You seem to have a preference for an approach in which you assume certain things are true about the world (e.g. y is a linear function of x), and then you derive some optimal prediction function, based on that…
Interesting -- if you've got a link, please post it.
So the extrapolation-type problem you describe (an input not near any of your training examples) is an issue. Unless you have a world model you believe in (i.e. you've done some science -- not just statistics), hard to…
I imagine Breiman was just talking about bagging-style parallel ensembles, when he was talking about variance reduction, not boosting-style sequential ensembles. Not long before he died, he was still actively trying to…
Hehe ok —- I also love Breiman’s Probability book. It’s really a standout on Ergodic theory. And Breiman et al.’s book on Trees is surprisingly rich, talking about all sorts of stuff besides trees.
You seem to have a preference for an approach in which you assume certain things are true about the world (e.g. y is a linear function of x), and then you derive some optimal prediction function, based on that…