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That's a particularly incompetent and irresponsible view of engineering. The key to modeling anything correctly is to be aware of exactly what assumptions your

by makeset 10y ago

That's a particularly incompetent and irresponsible view of engineering. The key to modeling anything correctly is to be aware of exactly what assumptions your choice of model corresponds to and how well they match the real-world processes underlying your data. So you've fitted some curve, but why that curve? What are the implications of assuming linearity between your input and output domains? What have you assumed about the distribution of noise over your predictions? How about over your input features? How would you expect the bias and variance of your predictions to degrade if any of these assumptions no longer held? As an engineer, if you don't understand the statistics behind your models enough to answer to such questions, their real-world applications aren't going to go far beyond wishful garbage-in-garbage-out number crunching.

geezerjay 10y ago
> So you've fitted some curve, but why that curve? Because it's the curve that's obtained by that particular minimization criteria, which is the minimization of the L² norm. If some other criteria was used, or other approximation function, then the result would also be valid. It appears that you are unaware that essentially all engineering in general, and whole field of computational mechanics in particular, is founded on what can be described as curve fitting. Whether it's plain old least squares approximation (in particular, moving least squares) or other techniques focused on the minimization of some other norm (Galerkin-type methods, for instance) the basis is all the same. > What are the implications of assuming linearity between your input and output domains? In short, because analytic functions and Taylor's theorem exist. Is it that hard? > As an engineer, if you don't understand the statistics behind your models enough to answer to such questions, their real-world applications aren't going to go far beyond wishful garbage-in-garbage-out number crunching. You know nothing about engineering, and somehow you're assuming that everything can only be valid if it's interpreted as a statistical problem. This isn't true, and it ignores complete knowledge field in physics and mathematics.

by makeset 10y ago

geezerjay 10y ago

> So you've fitted some curve, but why that curve? Because it's the curve that's obtained by that particular minimization criteria, which is the minimization of the L² norm. If some other criteria was used, or other approximation function, then the result would also be valid. It appears that you are unaware that essentially all engineering in general, and whole field of computational mechanics in particular, is founded on what can be described as curve fitting. Whether it's plain old least squares approximation (in particular, moving least squares) or other techniques focused on the minimization of some other norm (Galerkin-type methods, for instance) the basis is all the same. > What are the implications of assuming linearity between your input and output domains? In short, because analytic functions and Taylor's theorem exist. Is it that hard? > As an engineer, if you don't understand the statistics behind your models enough to answer to such questions, their real-world applications aren't going to go far beyond wishful garbage-in-garbage-out number crunching. You know nothing about engineering, and somehow you're assuming that everything can only be valid if it's interpreted as a statistical problem. This isn't true, and it ignores complete knowledge field in physics and mathematics.