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All machine learning models are capable of overfitting. Deep learning does not have a special relationship to overfitting, and in fact, there is some evidence t

All machine learning models are capable of overfitting. Deep learning does not have a special relationship to overfitting, and in fact, there is some evidence that the structural priors in complex deep networks actually inhibit overfitting and improve generalization [1]. Citing certain examples of misuse of deep learning by folks who "tweaked it till it worked" doesn't say anything about deep learning at large. The same team would do the same with a much less powerful model. Of course, being able to explain is a very valuable trait. But there's just no evidence that deep learning methods are any less amenable to interpretation than SVMs or decision trees. The latter models' "interpretation" is mostly stuff like "feature 543 and 632 were on" , while deep learning methods can not only do that, but can also synthesize examples of characteristics that the model looked for [2]. [1]: https://arxiv.org/abs/1611.03530 https://arxiv.org/abs/1611.03530 [2]: https://arxiv.org/abs/1605.09304 https://arxiv.org/abs/1605.09304

yorwba 9y ago
Then requiring algorithms to explain their reasoning should give neural networks an edge over other methods, if they are really easier to interpret.
gaius 9y ago
Deep learning does not have a special relationship to overfitting Yes and no. Let's say you have a time series and you know there is some pattern in it and you are interested in predicting what it will be at time t. A Fourier analysis would give you a formula to plug numbers into that anyone can understand. NN or DL might give you as good, or accounting for noise, even a slightly better prediction when backtested but now you need to explain "why did it do this" - to a financial regulator who is looking at insider trades, or a commission looking into why a piece of equipment failed killing the user (say, a self-driving car), or a newspaper who's noticed more people of race X are being turned down for mortgages. That's why I say it is dependent on the application of the algo.
YeGoblynQueenne 9y ago
>> [SVM and Decision Trees'] "interpretation" is mostly stuff like "feature 543 and 632 were on" SVMs are well-known black-box optimisers of numerical parameters so not more or less interpretable than neural nets, but decision tree learners are a different matter. The model they build is symbolic- a propositional logic theorem that can be output in a format that is directly inspectable and interpretable by a human being with an understanding of propositional logic. It's true that once decision trees grow beyond a certain size they're hard to interpret, but even so they can make a lot more sense than a heap of numbers in a large graph. Neural nets used for image processing are special in that they can output their activations as images that are directly interpretable by humans, but that's about the only application where we can really tell what a deep network is learning. Edit: >> All machine learning models are capable of overfitting. Aye, but deep nets by definition build extremely complex models with millions of parameters and so are especially prone to overfitting. Bias-vs-variance and all that.

yorwba 9y ago

Then requiring algorithms to explain their reasoning should give neural networks an edge over other methods, if they are really easier to interpret.

gaius 9y ago

Deep learning does not have a special relationship to overfitting Yes and no. Let's say you have a time series and you know there is some pattern in it and you are interested in predicting what it will be at time t. A Fourier analysis would give you a formula to plug numbers into that anyone can understand. NN or DL might give you as good, or accounting for noise, even a slightly better prediction when backtested but now you need to explain "why did it do this" - to a financial regulator who is looking at insider trades, or a commission looking into why a piece of equipment failed killing the user (say, a self-driving car), or a newspaper who's noticed more people of race X are being turned down for mortgages. That's why I say it is dependent on the application of the algo.

YeGoblynQueenne 9y ago

>> [SVM and Decision Trees'] "interpretation" is mostly stuff like "feature 543 and 632 were on" SVMs are well-known black-box optimisers of numerical parameters so not more or less interpretable than neural nets, but decision tree learners are a different matter. The model they build is symbolic- a propositional logic theorem that can be output in a format that is directly inspectable and interpretable by a human being with an understanding of propositional logic. It's true that once decision trees grow beyond a certain size they're hard to interpret, but even so they can make a lot more sense than a heap of numbers in a large graph. Neural nets used for image processing are special in that they can output their activations as images that are directly interpretable by humans, but that's about the only application where we can really tell what a deep network is learning. Edit: >> All machine learning models are capable of overfitting. Aye, but deep nets by definition build extremely complex models with millions of parameters and so are especially prone to overfitting. Bias-vs-variance and all that.