Live data from Hacker News

Machine Learning Crash Course: The Bias-Variance Dilemma

ml.berkeley.edu

61–63 of 63 posts

Re: Machine Learning Crash Course: The Bias-Variance Dilemma

#61
post #5

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.

it is interesting for sure - but, I have to point out that the containment systems actually did survive the quake. They didn't adequately plan for the tsunami. There may have been no distinction in the analysis, but I wouldn't infer that surviving the quake, and the survival of a potential tsunami were the same piece of analysis.

Re: Machine Learning Crash Course: The Bias-Variance Dilemma

#62
post #37
post #12

Earlier 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?

Definitely.

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

#63
post #49

Earlier 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…

Ultimately, everything is a probabilistic model. It'd be ridiculously impractical to try to incorporate all of one's knowledge into an analytic model. Instead, we generalize as appropriate and encapsulate the rest as a random variable, sometimes called "error". Plus, God plays dice and all that.
Post reply on HN