Wow, the discussion on the Fukushima civil engineering decision was pretty interesting. However, I find it surprising that the engineers simply overlooked the linearity of the law and used a nonlinear model. I wonder if there were any economic / other incentives at play, and the model shown was just used to justify the decision? Regardless, that post was a great read.
Machine Learning Crash Course: The Bias-Variance Dilemma
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Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#62Earlier quoted context omitted.
Yes, this is both uninteresting and true. The whole field of ML exists precisely because many things are too complex to model directly.
too complex for humans to create the models unassisted?
Look at the ImageNet ICVLR competition. Hand-built models can't approach SoTA results built with CNNs.
Re: Machine Learning Crash Course: The Bias-Variance Dilemma
#63Earlier quoted context omitted.
The irreducible error is unrelated to the bias/variance trade-off. It's part of the overall error of course, but the bias/variance error is in addition to that. Unless I am misunderstanding your point.
Sorry, I did not mean to be that they were the same. And, to that point, I was greatly projecting based on my beliefs that have grown. The number one thing I keep having to re-stress and learn is that these are probabilistic models. So, if anything, I only meant they were related in that they are both good targets to internalize when working with ML. If there are better targets, I'm definitely interested in learning…