4 ms·
Here is a quote from Segre's new biography of Fermi: "When Dyson met with him in 1953, Fermi welcomed him politely, but he quickly put aside the graphs he was b
by raincom 6y ago
Here is a quote from Segre's new biography of Fermi: "When Dyson met with him in 1953, Fermi welcomed him politely, but he quickly put aside the graphs he was being shown indicating agreement between theory and experiment. His verdict, as Dyson remembered, was "There are two ways of doing calculations in theoretical physics. One way, and this is the way I prefer, is to have a clear physical picture of the process you are calculating. The other way is to have a precise and self-consistent mathematical formalism. You have neither." When a stunned Dyson tried to counter by emphasizing the agreement between experiment and the calculations, Fermi asked him how many free parameters he had used to obtain the fit. Smiling after being told "Four," Fermi remarked, "I remember my old friend Johnny von Neumann used to say, with four parameters I can fit an elephant, and with five I can make him wiggle his trunk." There was little to add." (p.273)
One can fit an elephant with four free parameters. That's the story of these curve fitting exercises.
- Grakel 6y agoI ask this in all seriousness- what?
- strstr 6y agoIt’s a classic story about models like these (ones with many parameters).
- deleted 6y ago[deleted]
- yeezyseezy 6y agoIn regression and curve fitting, you can add additional, often spurious parameters, and get a closer fit. A better fit does not mean you have a better model, it usually means the opposite, that you've overfitted the model, and that it's completely useless for forecasting and predictive purposes. This is why in machine learning, researchers are careful to separate training and testing data. This is just physicists roasting each other over the same issue.
- bnveg 6y agoThe model shown in the article is cross-validated.
- drjasonharrison 6y agoAm I right in interpreting their screenshot that their best found model has 22 parameters?!
- mcphage 6y ago22 parameters, to predict 25 bits, and get 87.5% accuracy! Which is just under 22!
- bnveg 6y agoThis is out of sample performance. Only 4 models were compared outside the train domain. Also I don't see where you see 22 parameters, the complexity of a model is not defined as its number of parameters.