By Spencer Norris, Data Scientist, Independent Journalist.This article was originally published on OpenDataScience.com.Let’s see if we can start down that direction by laying the groundwork for the very fundamentals of our understanding of machine learning – namely, what actually constitutes a machine learning problem?.It’s kind of strange question that you might think you know the answer to, but it actually has a very formal definition that we’ll outline here.The most important step you can take is to start by asking yourself: do I think there’s a pattern?The fundamental assumption that underlies all machine learning problems is that there is a pattern..That pattern we’re looking for is some function f, which maps some input X onto an output Y..We’ll formalize this concept later on, but for now, take it at face value: we’re trying to formulate our hypothesis based on what is probably the case..There’s no guarantee that g is f – and in machine learning, we almost never have that guarantee – but this is his best guess based on the evidence that is available.If there was more evidence available, we might arrive at a different conclusion..In practice, you’ll need to determine how much room for error you can allow for in your algorithm based on your requirements (something we’ll talk about later).If it were a different agent looking at the evidence, they might suggest a totally different set of suspects.. More details