What if a model said no one will win the election? That model would be rejected immediately, but what if it wasn’t wrong? What if the learning picked up a signal from historic data that knew the election would be contested and no one wins? That’s not an unprecedented outcome when a “hanging chad” is a known feature. Perhaps the model predicts the US courts will decide. Plausible. Probably unlikely. Someone always wins eventually so what’s the ground truth – was the model right or wrong?
Perhaps the wrong question was asked; are election outcomes really binomial? Inherently with more than two political parties running, the results aren’t binary- they win or not. Elections can be truly a multinomial classification; however, we rarely treat elections as multinomial, but rather usually binomial. Right Mr. Nader? Right Mrs. Stein? Thousands of Markov chain simulations are cool on a random walk to the polls but there’s always the drunk leaning on the lamppost.
What causes bad predictions and why is it difficult for not only humans but machines to predict the outcome? So many reasons beyond the scope of this blogivation, which we will tell you in Squark Symmetry, but here are a few to get started:
There are so many other reasons why election probabilities are just that, probabilities, that may or may not happen. People fail to clean data; they overclean data, they don’t use enough observations. Sometimes analysts, despite best intentions, just don’t know what they don’t know. Recently on LinkedIn I saw some “expert” with an agency produce an inflammatory Covid related correlation analysis, and the guy didn’t even make the data stationary. Doh!
The cool thing about Squark is we know this and a lot more too. When we invented no code predictive analytics, we created a powerful entire automation, with deep tech sub-automations built it, that help reduce and work around the programmatically solvable issues above and many more.
We started with a vision to democratize prediction for business users by putting an advanced AI capability into their hands, without coding. We’ve done it now, and we want it to be affordable and powerful, so everyone can use it, even politicians. That is, if we would actually sell it to them. But like Jack Nicholas said in that Tom Cruise movie, perhaps they “can’t handle the truth!”
Squark is a no-code predictive analytics SaaS that automatically analyzes business data you work with every day to predict customer outcomes, uncover new and unforeseen business opportunities, and mitigate risk. It is a simple and easy-to-use AI software that doesn’t require data science nor technical expertise. In a matter of clicks, make confident, data-backed decisions about what your customers will do next and understand why to increase your business impact.