Random Forest
topic on 2 shows · 4 statements across 4 episodes
the MAD Podcast 
the a16z Podcast
4 statements about Random Forest, every show
Branson argues random forests should be the baseline for all ML models
“They should be the baseline everyone should start with for everything, right? And you should always compare your information gain above a random forest that could be depressing for a long time.”
Biewald: Traditional ML methods like boosted trees remain a large share of W&B usage
“A lot of people still running, you know, boosted trees or, you know, random forests inside of weights and biases. So we, you know, it's like actually huge. We should probably do a block, but it's still a big fraction of our You know, of our user base.”
Pande: Graph convolutions outperform random forest models in drug design
“And actually if you apply graph convolutions as Evan Feinberg did in this ACS Central Science paper in 2018, actually you can have a huge impact in terms of prediction comparing typical machine learning methods like random forest, which is a typical state of t…”
Michael Schmidt: Fitting data with complex machine learning models is solved
“So, one interesting thing is it's actually really, really easy to fit data. It's actually, it's quite boring. You know, you have, you know, a really large, complex model. Maybe it's a neural network. Maybe it's a random forest. Or maybe it's just a really larg…”