> In relation to this article, it says that when you build a model that fits the data, it's not enough. It's a further requirement that the model makes testable

> In relation to this article, it says that when you build a model that fits the data, it's not enough. It's a further requirement that the model makes testable predications that turn out to be correct. Models that make accurate predications are valuable. Models that aren't then tested by checking their predictions are, by definition, untested. the problem with the article is that it lumps in many 'models' with wholly untested ones undeservedly, saying And no, that does not mean simply training your model on half of your data set and showing that you can effectively explain the other half of your data. That is hypocritical because this is more or less precisely what scientists, following the "scientific method", are doing, albeit many times slower than a computer with a parametric model. > I'd be interested to know why you describe making falsifiable models is just "a learning algorithm." what happens in some piece of the world over some duration of time can be represented as a function from that piece of the world's initial state to its final state. in the sciences, we try to estimate such functions. the scientific method is a very simple algorithm for doing so: generate a hypothesis, compute N "testable predictions" f(x_i)\approx y_i, and then accept your hypothesis if they all turn out to be correct.