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That doesn't seem right to me. In both cases you have a model and are just searching for optimal parameters considering the bias/variance tradeoff. There may
by nightski 5y ago
That doesn't seem right to me. In both cases you have a model and are just searching for optimal parameters considering the bias/variance tradeoff. There may be a few instances of a neural network or other ML model being set up to dynamically change it's architecture during training but that seems to me like it would not work out well at all.
If anything in specific cases the statistical model (if Bayesian) is more comprehensive in that it doesn't try to find a point estimate of the parameters but instead forms a full distribution around the plausibility of the parameters.
- sjg007 5y agoYou can have Bayesian DNNs.
- whimsicalism 5y agoNot easily. I don't think the literature is very incredible on this - how do you define a prior over all of the parameters of an NN?
- sjg007 5y agoThere’s a vast literature you can read.
- whimsicalism 5y agoI have read some of the literature - ie. the bayes by backprop method, etc. It doesn't seem like getting a posterior over the parameter space of a neural network is tractable as of now.
- sjg007 5y agoIt ain't tractable in general either.
- sgt101 5y agoSo I was thinking like this - if you have a data set with 200000 variables and 70,000 examples how would you find a model without using a search process? On the other hand if you have 20 variables and or if you understand which of the 200000 variables are the ones to worry about then you can build a model by hand. I guess that also statisticians are working to summarise or create insight about data while ML is working to create a prediction (although statistical models can be used to do that too).
- bart_spoon 5y agoWhy do you think that statisticians only build models by hand? Dimension reduction techniques are employed in statistical learning as well.