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A mathematical formula that predicts US elections with 87.5% accuracy
- mywittyname 6y agoUhhhh...This is the dumbest ad article I've read in a while.
- Imnimo 6y agoI would expect to be able to get 100% (on the training data) on this task. Shouldn't any sequences of election victories be easily represented with multiple sin functions? I feel like the message I'm supposed to take from this demo is "Look how good TuringBot is! They can automatically find a function to match this data!" But the actual message I'm getting is "Symbolic regression is too hard for TuringBot!"
- frabert 6y agoIndeed. Just perform an FFT on the data and encode the resulting coefficients in a closed formula.
- bnveg 6y agoThis is a Pareto optimization with a limit on the size of the formulas. Sure, with a formula of arbitrary size you can fit anything with 100% accuracy, but the task is much harder if what you are looking for are short models.
- kevinventullo 6y agoThat's nothing, I can generate a degree 44 polynomial that predicts US elections with 100% accuracy!
- bnveg 6y agoNow do that with cross validation and get a better result than the reported
- cjohnson318 6y agoExcept that the Democrats of 1940 were much different than the Democrats of 1990, and the Democrats of 2020.
- RhysU 6y agoEverything is a time series.
- cjohnson318 6y agoI thought this was going to be about Lichtman[0]. [0](https://en.wikipedia.org/wiki/The_Keys_to_the_White_House https://en.wikipedia.org/wiki/The_Keys_to_the_White_House)
- ykevinator 6y agoThe year is the input? This is data fitting, agreed?
- raincom 6y agoHere 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.
- darksaints 6y agoThis commits the same fallacy that exists all over the place for time series data. This bites almost anybody who tries using time series methods for predicting equities returns. Since you have a literal dependency between one data point and the next, you can't train your model using randomized data. So the default that people jump to is to segment their data with earlier data being the training set, and later data being the test set. If your data is the result of a very well understood and controlled process, this works fabulously (see ARIMA and variants). But the more that the data relies on significant noise, whether that is pure randomness or Brownian motion (very likely the case with elections), that methodology breaks down spectacularly. All you end up doing is overfitting to your test set instead of your training set.
- bnveg 6y ago>Since you have a literal dependency between one data point and the next, you can't train your model using randomized data This is why the train/test split was not randomized, but sequential.
- woopwoop 6y agoMaybe the other commenters tearing into this are on a higher plane of irony from me, but I want to point out that I'm 99% sure the article is meant as a joke.
- elektor 6y agoThe article is hosted by a real software product so it’s not likely this is satire.
- mcphage 6y agoWell, I really hope so. It doesn’t really read as a joke, but on the other hand, they’re bringing their impressive software to bear to attempt to predict 25 bits of data, and can’t even do that very well. So I hope so.
- deleted 6y ago[deleted]
- defertoreptar 6y agoNow repeat the same procedure on randomized data and see how often you can match or surpass this accuracy.
- bnveg 6y agoAs a rule of thumb, you cannot. I have tried to fit the Mona Lisa with the software (brighness as a function of x and y) and could not find anything. It performs poorly when no structure exists.
- purplezooey 6y agoNicolas Cage movies are missing