The difference is in how the output is evaluated. An ML model is evaluated empirically. You compare to real world results and get an accuracy measure. An ML mo

The difference is in how the output is evaluated. An ML model is evaluated empirically. You compare to real world results and get an accuracy measure. An ML models tells you what something should be. And if it’s a good model, you will get a pretty high frequency of that model telling you what the thing is. Statistics does something entirely different. It tells you what something could be. If you flip a coin, an ML model will tell you if it’s heads or tails. Statistics will tell you how often it will be heads or tails. Another way to think about it is the difference between probability and likelihood. Probability is measured by your theoretical priors and hypotheses. Likelihood is measured by the results of actual trials. The probability of a fair coin landing on heads is .5 But the likelihood of that happening isn’t actually .5 because pure frequentist probabilities depend on some fundamentally problematic things. Like a performative infinite number of trials. The actual line between ML and statistics is really blurry because all useful statistical models are at least a little Bayesian. Priors get updated with each trial. This is essentially machine learning. Outside of mostly bad/soft sciences (sociology, psychology, neuroscience, nutrition, and climatology are all pretty godawful about abusing classical statistics) pure frequentist statistics don’t get used much because they are really only useful for getting papers published and generating squawking headlines. Most useful statistical methods are machine learning methods. Specifically, they are applied Bayesian methods with weak, randomized priors. Which is exactly what ML is. I sound like I hate statistics. I don’t really. ML models can do a bunch of wacky things. There isn’t a coherent theory behind an ML model. You could point a very good classifier at your wife, and it might tell you [(bird,.1), (apple,.3), (woman,.9)] That’s not a realistic interpretation of what could be. It just happened to get that correct. A really excellent ML model in 2016 could’ve given the following result for president [(trump,.8), (obama,.6), (rock,.5)]. And after the fact when we can compare it to what happened, it would seem accurate. But that doesn’t tell us the range of possibilities in our future. The reality in 2016 was that we weren’t going to elect a fucking rock as president. There was no chance of that. There is zero chance that if I point my camera at my girlfriend, she might actually be a potato. Yeah, the ML model might be right because it has guessed right, and that’s often all we care about. But if I need to know what my chances are of my girlfriend becoming my wife or the mother of my children. That’s where we need statistics. An ML model can’t have those kinds of priors baked in unless you force it. And if you do that you’re just paying someone to do some really expensive Bayesian regression.