Linear Model
topic on 2 shows · 3 statements across 2 episodes
the Knowledge Project 
Latent Space
3 statements about Linear Model, every show
Chu: Descriptive Models Fail to Beat Linear Baselines on Causal Biology
“Models that are trained on descriptive data do not yet outperform linear models on causal tasks, perturbational tasks, what we call counterfactual tasks.”
Wang: Foundation models on causal data will beat linear baselines
“I believe that foundation model or other more complicated AI models that trend on the right data will outperform these linear models in harder tasks, particularly in generalization tasks.”
Page: Rely on models when crowds agree, but investigate when predictions diverge
“What you should do instead is if the linear model and the people are close, you know, the prediction's You probably should go to the linear model because it's really well calibrated, right? It's probably gonna, you know, be better. But if they're far apart, if…”