There are objective ways of knowing whether a model is on the right track - testing generalizability based on a prior predictions of new data can be very helpful in ground proofing models. It's not rocket science, but it's powerfully convincing. And quantitative vs. argumentative. Nested learning in wrappers optimizing data representation, feature selection, parameter optimization, model selection, all in one, perfectly testable - and better than curve-fitting and least-squaresizing everything! Also allows non-linearity - arbitrary but testable.
Your careful discussions of modeling and your criticisms of the typical tactics have kept me interested in your work, and has me interested in several of your IPAK edu courses. I just need to pull the trigger and try one out.
See this article (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719747/) about scientific consensus. Especially the quotes of Michael Crichton, like this one: “In science consensus is irrelevant. What are relevant are reproducible results. The greatest scientists in history are great precisely because they broke with the consensus. There is no such thing as consensus science. If it’s consensus, it isn’t science. If it's science, it isn't consensus. Period.” Fitting data to a consensus model is not science. If the data are reproducible, and don’t fit the model, then a scientist should propose a new model.
Deleted comments - could be offended, scared, insulted, ashamed, uncomfortable, no idea. But I imagine those are the usual reasons. I would delete a comment if I thought it was a kind of harassment or active threat. But I haven’t observed any of your comments to be like that. Not even close.
Mike Yeadon is a big fish - with F You money and everything. Probably doesn’t care. That’s my guess. But it’s all just speculation on my part.
There are objective ways of knowing whether a model is on the right track - testing generalizability based on a prior predictions of new data can be very helpful in ground proofing models. It's not rocket science, but it's powerfully convincing. And quantitative vs. argumentative. Nested learning in wrappers optimizing data representation, feature selection, parameter optimization, model selection, all in one, perfectly testable - and better than curve-fitting and least-squaresizing everything! Also allows non-linearity - arbitrary but testable.
Your careful discussions of modeling and your criticisms of the typical tactics have kept me interested in your work, and has me interested in several of your IPAK edu courses. I just need to pull the trigger and try one out.
See this article (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719747/) about scientific consensus. Especially the quotes of Michael Crichton, like this one: “In science consensus is irrelevant. What are relevant are reproducible results. The greatest scientists in history are great precisely because they broke with the consensus. There is no such thing as consensus science. If it’s consensus, it isn’t science. If it's science, it isn't consensus. Period.” Fitting data to a consensus model is not science. If the data are reproducible, and don’t fit the model, then a scientist should propose a new model.
Intentional failure being built into the method, well said.
Who’s Girardot?
Deleted comments - could be offended, scared, insulted, ashamed, uncomfortable, no idea. But I imagine those are the usual reasons. I would delete a comment if I thought it was a kind of harassment or active threat. But I haven’t observed any of your comments to be like that. Not even close.
Mike Yeadon is a big fish - with F You money and everything. Probably doesn’t care. That’s my guess. But it’s all just speculation on my part.