What’s the difference between statistics and machine learning?
This may sound a little like trivializing, but don&#x27;t we have to know what &quot;statistics&quot; are and what &quot;machine learning&quot; is to say anything about the difference(s) between them?<p>Looking at this through even the lens of multinomial logistic regression, or of econometrics generally, I don&#x27;t think that &quot;statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns&quot; even makes sense <i>as a difference</i>. Any <i>prediction</i> is an <i>inference</i> about the population of future events, or of contemporary events not present in the <i>sample</i>. You plug 1000s of events each described by 100 columns into a logistic regression, and you&#x27;re hoping to get something predictive out of it. Further, as nice as the idea is that you can tease out &quot;factors&quot; from your 100 columns, you don&#x27;t have to look at &quot;3.3375905e-5 x (spent five years before age 18 in a smoker&#x27;s home)&quot; for very long to wonder how much &#x27;explanation&#x27; you&#x27;re getting out of the terms in the exponents of your probability functions.<p>I still can&#x27;t resist tweaking ML enthusiasts and data scientists: Statistics is what people who know what they&#x27;re doing are doing. Machine learning is the rest!