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Nice comment, but there's more nuance than you claim. > You made no assumptions about your data, about the distribution of it, any of it. You just followed an

Nice comment, but there's more nuance than you claim. > You made no assumptions about your data, about the distribution of it, any of it. You just followed an algorithm. You made implicit assumptions that you are now unaware of, which might come and bite you later (eg: using zip codes or names as a proxy for race in credit scoring models, neural networks overfititng to texture and classifying a leopard print couch as a leopard). This means that you are liable to overfit to the data, and generalize poorly, or in ways you are not supposed to. This is what leads to the perspective that "machine learning" might end up being a powerful tool for laundering bias: https://idlewords.com/talks/sase_panel.htm https://idlewords.com/talks/sase_panel.htm

cwyers 7y ago
I think what you're saying is orthogonal to what I'm saying. Yes, ZIP codes can be a proxy for race in models dealing with credit scores (and recidivism, which is also a really bad place to put racial bias), as an example. But if I put it in a mixed-effects model, it shows the same bias, and a mixed-effects model is just an extended version of linear regression. Both statistical and ML models suffer from the problem you're stating. What you have not made any assumptions about in a random forest is about the distribution of the data you're looking at. One example of a case where the assumptions that bog-standard OLS makes about your data can cause you problems is zero-dominated data -- data with a lot of zeros in it. Basically any time you're trying to make predictions about things that are rare in your measured population. OLS does a bad job on zero-dominated data. If you throw a zero-dominated dataset into a random forest, you will get back better answers than if you use OLS on zero-dominated data. To be clear: there are strategies for dealing with zero-dominated data using statistical inference. You don't have to resort to non-inferential learning just because you have data that doesn't look like a bell curve. But machine learning is a powerful way to get pretty good results on a lot of problem spaces without having to understand the probability function involved (or in cases where the probability function is too complicated to be tractable computationally, like the probability function that determines the color of pixels in a dataset where you're classifying dogs versus cats).
- ssivark 7y ago
  > machine learning is a powerful way to get pretty good results on a lot of problem spaces without having to understand the probability function involved The NFL theorems essentially state that for every dataset on which an algorithm generalizes well, there is another on which it generalizes poorly. So, there are always implicit biases, even for a random forest model (eg: if you try to model Boolean functions with a random forest, those functions which can be approximated effectively with few trees will form a set of very small measure. Specifically, it is my intuition that those which have many terms in the sum of products form might need many trees to approximate). It then becomes a question of whether the bias of your models are compatible with the dataset/domain under consideration. See this Minsky-Sussman koan: http://www.catb.org/jargon/html/koans.html#id3141241 http://www.catb.org/jargon/html/koans.html#id3141241 > Both statistical and ML models suffer from the problem you're stating. And that is precisely why I don’t see how ML algorithms are “more powerful” than statistics in any way, as per your claim.
  - cwyers 7y ago
    > And that is precisely why I don’t see how ML algorithms are “more powerful” than statistics in any way, as per your claim. Well I'm glad you don't see that, but I'm a bit confused why you think I said it because I didn't say it and don't believe it. I said that ML is "powerful," not "more powerful."

cwyers 7y ago

I think what you're saying is orthogonal to what I'm saying. Yes, ZIP codes can be a proxy for race in models dealing with credit scores (and recidivism, which is also a really bad place to put racial bias), as an example. But if I put it in a mixed-effects model, it shows the same bias, and a mixed-effects model is just an extended version of linear regression. Both statistical and ML models suffer from the problem you're stating. What you have not made any assumptions about in a random forest is about the distribution of the data you're looking at. One example of a case where the assumptions that bog-standard OLS makes about your data can cause you problems is zero-dominated data -- data with a lot of zeros in it. Basically any time you're trying to make predictions about things that are rare in your measured population. OLS does a bad job on zero-dominated data. If you throw a zero-dominated dataset into a random forest, you will get back better answers than if you use OLS on zero-dominated data. To be clear: there are strategies for dealing with zero-dominated data using statistical inference. You don't have to resort to non-inferential learning just because you have data that doesn't look like a bell curve. But machine learning is a powerful way to get pretty good results on a lot of problem spaces without having to understand the probability function involved (or in cases where the probability function is too complicated to be tractable computationally, like the probability function that determines the color of pixels in a dataset where you're classifying dogs versus cats).

ssivark 7y ago
> machine learning is a powerful way to get pretty good results on a lot of problem spaces without having to understand the probability function involved The NFL theorems essentially state that for every dataset on which an algorithm generalizes well, there is another on which it generalizes poorly. So, there are always implicit biases, even for a random forest model (eg: if you try to model Boolean functions with a random forest, those functions which can be approximated effectively with few trees will form a set of very small measure. Specifically, it is my intuition that those which have many terms in the sum of products form might need many trees to approximate). It then becomes a question of whether the bias of your models are compatible with the dataset/domain under consideration. See this Minsky-Sussman koan: http://www.catb.org/jargon/html/koans.html#id3141241 http://www.catb.org/jargon/html/koans.html#id3141241 > Both statistical and ML models suffer from the problem you're stating. And that is precisely why I don’t see how ML algorithms are “more powerful” than statistics in any way, as per your claim.
- cwyers 7y ago
  > And that is precisely why I don’t see how ML algorithms are “more powerful” than statistics in any way, as per your claim. Well I'm glad you don't see that, but I'm a bit confused why you think I said it because I didn't say it and don't believe it. I said that ML is "powerful," not "more powerful."