Basically: "instructions", that become more simple over time. There are some nuances to, say, R^2. But the concept that it's "how much variance is explained by the model" isn't difficult to comprehend...or apply.
Let me clarify that I'm not saying it's unimportant to understand the underlying mathematics behind these processes. After all, someone has to design these things so that the layman can actually apply them. What I, and it seems others, are arguing is that it isn't necessary to have a deep understanding of the algorithms to get insight from their usage. Some creative person creates the tool, and other creative people figure out its best uses. They are rarely the same people.
I'll add: I'm not sure why you're down-voted. This community seems to be developing those bad habits of disagree = down vote.
That's a good point, there has to be a line drawn at some point, and that line probably depends on the user. It seems like documentation and communication are important for making making that boundary a bit softer too. E.g. looking at the mathematical definition of R2 isn't as immediately clear as describing it as "variance explained".
Basically: "instructions", that become more simple over time. There are some nuances to, say, R^2. But the concept that it's "how much variance is explained by the model" isn't difficult to comprehend...or apply.
Let me clarify that I'm not saying it's unimportant to understand the underlying mathematics behind these processes. After all, someone has to design these things so that the layman can actually apply them. What I, and it seems others, are arguing is that it isn't necessary to have a deep understanding of the algorithms to get insight from their usage. Some creative person creates the tool, and other creative people figure out its best uses. They are rarely the same people.
I'll add: I'm not sure why you're down-voted. This community seems to be developing those bad habits of disagree = down vote.