Wasn’t sure if the flair mathematics was more appropriate or not. Can you what is it and what is its purpose as opposed to statistics?
In: Engineering
First of all, there are some similarity between Machine Learning (ML) and stats, there are even some definite overlap, e.g. Linear Regression. Hence, the memes:
https://miro.medium.com/max/700/1*x7P7gqjo8k2_bj2rTQWAfg.jpeg
https://miro.medium.com/max/2560/1*mXeEWBymq-UXPXF2Oai5mg.jpeg
https://i.redd.it/4f71u8ti5hg31.jpg
What makes things more complicated is that ML is an extremely rapidly growing field, what it IS changes from month to month (usually growing). So it is kinda hard to tell the difference.
So here is my extremely simplified version:
* The purpose of statistics is that so HUMAN can understand the data
* The purpose of ML is to make PREDICTION.
For example, you are a CEO, and sales is down this month. If you want to know why, so you can do something about it, you get a statisticians. If you want to know what is sales for next month, so you know how much stock you have to order, you get an ML expert.
But here is a good example where things get complicated. Explainable AI and causal inference are definitely under ML, although they are supposed to answer the “why” question, on top of making prediction. There are also things like clustering, that also attempts to give human insights into the data.
A better explanations is that some tools, like neural network, are born out of the ML field, and regardless of what is used for. It will always be considered as ML, not stats.
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So here’s my summary. In the past, everything is stats. Then some people are interested in making predictions using the tools in stats, so ML was simply a subfield of stats. Then a number of ML specific tools (like neural network) were made. So now, anything that uses those tools, are considered as ML, regardless or purpose (e.g. XAI and causal inference). Worse, people are enhancing those ML tools with classical stats tools (e.g. Bayesian + neural network). In those cases, more often than not, the ML labels stick.
(I know this is a bad history, because ML is born out of CS and AI, not stats. But ELI5)
So, the distinction between ML and stats is more like the distinction between a main course and a dessert. It is hard to make hard and fast rule, but you can feel it.
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