I’ve been spending a bit of time on machine learning lately. But when it comes to classification or regression: it’s basically the reversing camera on your car.
Let me elaborate: machine learning, like a reversing camera, is awesome. Both things let you do stuff you already could do, but faster and more often. Both give you insights into the world around you that you may not have had without them. However, both can give a more narrow view of the world than some other techniques (in this case, expanded statistical/econometric methodologies and/or your mirrors and checking your blindspots).
As long as everything around you remains perfectly still and doesn’t change, the reversing camera will let you get into a tight parking spot backwards and give you some insights into where the gutter and other objects are that you didn’t have before. Machine learning does great prediction when the inputs are not changing.
But if you have to go a long way in reverse (like reversing down your driveway- mine is 400m long), or things are moving around you (other cars, pet geese, STUPID big black dogs that think running under your wheels is a great idea. He’s bloody fine, stupid mutt): then the reversing camera alone is not all the information you need.
In the same way, if you need to explain relationships- because your space is changing and prediction is not enough- then it’s a very useful thing to expand your machine learning toolbox with statistical/econometric techniques like hypothesis testing, information criteria and solid model building methodologies (as opposed to relying solely on lasso or ridge methods). Likewise, causality and endogeneity matters a lot.
So, in summary machine learning and reversing cameras are awesome, but aren’t the whole picture in many cases. Make your decision about what works best in your situation: don’t just default to what you’re used to.
(Also, I’m not convinced this metaphor extends in the forwards direction. Data analysis? You only reverse, maybe 5% of the time you’re driving. But you’re driving forward the rest of the time: data analysis is 95% of my workflow. Yours?)