7 ms·

The Mixed Track Record of Machine Learning Algorithms

52 points by briatx 8y ago

fathead_glacier 8y ago
I find this article moot. They are basically saying that applying a blank ML stamp can yield unsatisfying results on your data. That ought to be obvious and seems unnecessarily repetitive.
pjmorris 8y ago
> “Machine learning algorithms will always identify a pattern, even if there is none,” he says. I think that's perfectly said. Humans are prone to the same thing, but we've developed better coping mechanisms.
- mlthoughts2018 8y ago
  It’s a bit foolish though, because “regression” will always identify a pattern too, or many other simplistic models. In ML, techniques to avoid overfitting or reporting spurious relationships are a first-order, 101 topic, especially among the type of ML engineer a hedge fund might hire (they are not hiring data science hacks). On the flip side, I worked in a quant finance firm before that mostly did factor investing with some twists, and the overall statistical rigor was embarrassing. Even with simple regressions, nobody was asking basic robustness questions, p-hacking was daily life, directly comparing t-stats from different univariate model fits was considered “advanced feature selection.” If a firm is goimg to do bad stats, they don’t need machine learning for that.
  - pjmorris 8y ago
    I guess what I'm trying to say is that algorithms can't tell whether they're fooling themselves. Someone has to apply the 101 techniques for testing fit, etc. Humans at least have the opportunity, though, as you point out, they don't always take it.
    - candiodari 8y ago
      Maybe it's just me, but to me it seems like 99%+ of humans don't check whether they're fooling themselves, even when using statistics. Have you ever known anyone to check if the central limit theorem applies before taking an average ? I mean, we did it once when learning what it was and why you might want to check, but ... The problem with statistics is that they in theory don't work in the real world. For instance, if you check a statistical variable, great. Now you fix something in the real world and recheck your variable. BZZT wrong ! You can't measure a variable after you've tried to influence it, because obviously you're not measuring the same thing anymore. So there is (potentially) no relationship whatsoever between the measurement after the change and the measurement before. So ... statistics CANNOT correctly be used to improve things in the real world. But ... have you ever known anyone to use statistics any other way ? Also: we don't actually have anything better. The thing is ... it mostly works in practice, though you can come up with examples where it doesn't. And of course you can do things very wrong, as you're just adding, multiplying and so on. That works on any set of numbers. The thing about machine learning is that a well designed machine learning algorithm contains far less details about the problem than a statistical model. So people far less versed in the problem being analyzed can improve things more using machine learning than by using statistics. But the potential maximum improvement you could ever hope to make, statistics is going to be higher. Compare a second-degree regression to and LSTM for a time series. ASSUMING the statistical model works at all, it'll beat the crap out of the LSTM. But the LSTM will sort-of succeed in nearly all cases. So if the variable fits the information you stuck into your statistical model there's no beating that model (in this case that the data is generated by a second-degree process with a not-too-close-to-zero determinant) Issue for the future is that all interesting problems are beyond the comprehension of any human, so ... machine learning will win. This means humans can't make statistical models for them either. It'll win, not because it is always the best solution, but because for so many problems you might as well say for all problems we will never find anything remotely optimal or understand enough to even figure out how to apply statistics to it.
      - pjmorris 8y ago
        > Issue for the future is that all interesting problems are beyond the comprehension of any human, so ... machine learning will win. I agree that there's a set of problems that are both beyond human comprehension and interesting to humans. Specifying them, measuring the results of algorithms to solve them, and paying for the results will probably have to remain within human analytical capability, or you wind up with Skynet (unlikely), or some analog to the 'gray goo' problem, where machines are optimizing with unintended consequences.
- 0-_-0 8y ago
  That sentence is not true in the presence of good regularization. And doing proper regularization is a major part of machine learning as a discipline, just like having test and training sets to see if you overfit the data. So “Machine learning algorithms will always identify a pattern, even if there is none” is only true if you don't follow the basic best practices of machine learning.
- crunchlibrarian 8y ago
  Most of machine "learning" in real world use seems to be humans fiddling with weights until they get the magic number they want.
bitL 8y ago
Most of those replies sound like: "we are experts, we know better" and stating badly outdated facts that probably passed through marketing department on the way to those experts. I hope no progressive person wants to work for them, but rather to compete with them to drive them out of business, a typical pattern repeating whenever somebody gets too cocky about their abilities.
geebee 8y ago
“Machine learning algorithms will always identify a pattern, even if there is none” Is this partly a problem with interpretation? Let's say I do a binary (supervised) classification with an algorithm that is also capable of assessing probabilities. If I generate a data set consisting of a randomized bag of words, and randomly assign them to 0 and 1 categories, and run it through a supervised ML classifier, then yeah, everything in the test set will get assigned to something. But if you look at the probability estimates resulting from the ML, you'd almost certainly see something that indicates a high degree of randomness in the assignments (various techniques such as cross validation, or probabilities that indicate a high degree of uncertainty for almost all of the predictions). I'm not sure this is a problem with the algorithm itself, because the output from many of these algorithms does indicate low predictive value.
- sdenton4 8y ago
  Check out this classic paper on deep learning with randomized labels: https://arxiv.org/abs/1611.03530 https://arxiv.org/abs/1611.03530 Spoiler: the neutral net thinks it's doing a really good job!
  - geebee 8y ago
    Thank you for the link. I'll read this paper. I'm hoping to reply but the thread may be stale by the time I do. Right now, my thoughts are: if it is easily fitting, what are the assignment probabilities? Are we getting 90%+, or is it fitting easily, but to much lower probabilities. Also, is there a big difference between neural nets and other algorithms like RF? The paper certainly does appear to address the question of categorizing completely randomized input: From the abstract "...our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a ran- dom labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by com- pletely unstructured random noise.
    - geebee 8y ago
      I'm going off a first pass through the paper, but it appears that what this paper shows is that the training error can be 0 on an entirely randomized data set, but the generalization error - the difference between the error on the test set and the training set, does increase dramatically as label corruption increases. My understanding is that cross validation does multiple combinations of splitting the input data into test and training sets... so if cross validation measures the generalization error, wouldn't this catch the low predictive value resulting from randomization of labels or input? I'm not saying the paper doesn't have value, but I think it's more about the fact that neural nets can obtain a training error of zero on randomized data, not a testing error (or generalization error, which represents the difference between training error and testing error, as far as I can tell). To be clear, I'm not an expert, and this is just what I gleaned from a first pass over the paper.
screye 8y ago
A good ML system has to be architect-ed to exploit known structure of the task that is being attempted. CNN's exploit spatial locality and LSTMs exploit temporal locality. The SOTA models are architect-ed with even stronger assumptions about the nature of the task. Methods like Neural Networks, Random Forests and SVMs when used as unconstrained universal function approximators for unstructured data only learn some non-linear polynomial/ exponential/logarithmic combination of data itself, without much nuance. It is critical to help a model out by constraining the space of models it searches over to find the right answer. I think, unless we figure out a way to constrain architectures to exploit specific traits of task they are trying to solve, (universal function approximator type) ML won't succeed in the same way that it has in vision / language. As it of now, the alternative is to use PGMs where the model is fully interpretable as a graph structured combination of explicitly parameterized random variables. PGMs work well with low data and give really good uncertainty estimates, to evaluate the quality of a model. PGMs of course suffer from the problem where they are excruciatingly slow for large datasets and require require a decent amount of prior knowledge about the problem to explicitly define the type of graph structure / random variables we are going to be using. I think ML is most certainly capable of solving this problem, but the community is probably waiting for another break through along the lines of AlexNet/LSTMs before that it the case.
- deleted 8y ago
  [deleted]
pighive 8y ago
Why are we seeing so many stories from bloomberg.com? What tier is bloomberg’s credibility and their sources’? What part of technology, or business is their journalism known for? I am new to US, but I think my questions are not senseless. Thanks.

52 points by briatx 8y ago

I find this article moot. They are basically saying that applying a blank ML stamp can yield unsatisfying results on your data. That ought to be obvious and seems unnecessarily repetitive.

pjmorris 8y ago

> “Machine learning algorithms will always identify a pattern, even if there is none,” he says. I think that's perfectly said. Humans are prone to the same thing, but we've developed better coping mechanisms.

mlthoughts2018 8y ago
It’s a bit foolish though, because “regression” will always identify a pattern too, or many other simplistic models. In ML, techniques to avoid overfitting or reporting spurious relationships are a first-order, 101 topic, especially among the type of ML engineer a hedge fund might hire (they are not hiring data science hacks). On the flip side, I worked in a quant finance firm before that mostly did factor investing with some twists, and the overall statistical rigor was embarrassing. Even with simple regressions, nobody was asking basic robustness questions, p-hacking was daily life, directly comparing t-stats from different univariate model fits was considered “advanced feature selection.” If a firm is goimg to do bad stats, they don’t need machine learning for that.
- pjmorris 8y ago
  I guess what I'm trying to say is that algorithms can't tell whether they're fooling themselves. Someone has to apply the 101 techniques for testing fit, etc. Humans at least have the opportunity, though, as you point out, they don't always take it.
  - candiodari 8y ago
    Maybe it's just me, but to me it seems like 99%+ of humans don't check whether they're fooling themselves, even when using statistics. Have you ever known anyone to check if the central limit theorem applies before taking an average ? I mean, we did it once when learning what it was and why you might want to check, but ... The problem with statistics is that they in theory don't work in the real world. For instance, if you check a statistical variable, great. Now you fix something in the real world and recheck your variable. BZZT wrong ! You can't measure a variable after you've tried to influence it, because obviously you're not measuring the same thing anymore. So there is (potentially) no relationship whatsoever between the measurement after the change and the measurement before. So ... statistics CANNOT correctly be used to improve things in the real world. But ... have you ever known anyone to use statistics any other way ? Also: we don't actually have anything better. The thing is ... it mostly works in practice, though you can come up with examples where it doesn't. And of course you can do things very wrong, as you're just adding, multiplying and so on. That works on any set of numbers. The thing about machine learning is that a well designed machine learning algorithm contains far less details about the problem than a statistical model. So people far less versed in the problem being analyzed can improve things more using machine learning than by using statistics. But the potential maximum improvement you could ever hope to make, statistics is going to be higher. Compare a second-degree regression to and LSTM for a time series. ASSUMING the statistical model works at all, it'll beat the crap out of the LSTM. But the LSTM will sort-of succeed in nearly all cases. So if the variable fits the information you stuck into your statistical model there's no beating that model (in this case that the data is generated by a second-degree process with a not-too-close-to-zero determinant) Issue for the future is that all interesting problems are beyond the comprehension of any human, so ... machine learning will win. This means humans can't make statistical models for them either. It'll win, not because it is always the best solution, but because for so many problems you might as well say for all problems we will never find anything remotely optimal or understand enough to even figure out how to apply statistics to it.
    - pjmorris 8y ago
      > Issue for the future is that all interesting problems are beyond the comprehension of any human, so ... machine learning will win. I agree that there's a set of problems that are both beyond human comprehension and interesting to humans. Specifying them, measuring the results of algorithms to solve them, and paying for the results will probably have to remain within human analytical capability, or you wind up with Skynet (unlikely), or some analog to the 'gray goo' problem, where machines are optimizing with unintended consequences.
0-_-0 8y ago
That sentence is not true in the presence of good regularization. And doing proper regularization is a major part of machine learning as a discipline, just like having test and training sets to see if you overfit the data. So “Machine learning algorithms will always identify a pattern, even if there is none” is only true if you don't follow the basic best practices of machine learning.
crunchlibrarian 8y ago
Most of machine "learning" in real world use seems to be humans fiddling with weights until they get the magic number they want.

bitL 8y ago

Most of those replies sound like: "we are experts, we know better" and stating badly outdated facts that probably passed through marketing department on the way to those experts. I hope no progressive person wants to work for them, but rather to compete with them to drive them out of business, a typical pattern repeating whenever somebody gets too cocky about their abilities.

geebee 8y ago

“Machine learning algorithms will always identify a pattern, even if there is none” Is this partly a problem with interpretation? Let's say I do a binary (supervised) classification with an algorithm that is also capable of assessing probabilities. If I generate a data set consisting of a randomized bag of words, and randomly assign them to 0 and 1 categories, and run it through a supervised ML classifier, then yeah, everything in the test set will get assigned to something. But if you look at the probability estimates resulting from the ML, you'd almost certainly see something that indicates a high degree of randomness in the assignments (various techniques such as cross validation, or probabilities that indicate a high degree of uncertainty for almost all of the predictions). I'm not sure this is a problem with the algorithm itself, because the output from many of these algorithms does indicate low predictive value.

sdenton4 8y ago
Check out this classic paper on deep learning with randomized labels: https://arxiv.org/abs/1611.03530 https://arxiv.org/abs/1611.03530 Spoiler: the neutral net thinks it's doing a really good job!
- geebee 8y ago
  Thank you for the link. I'll read this paper. I'm hoping to reply but the thread may be stale by the time I do. Right now, my thoughts are: if it is easily fitting, what are the assignment probabilities? Are we getting 90%+, or is it fitting easily, but to much lower probabilities. Also, is there a big difference between neural nets and other algorithms like RF? The paper certainly does appear to address the question of categorizing completely randomized input: From the abstract "...our experiments establish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a ran- dom labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by com- pletely unstructured random noise.
  - geebee 8y ago
    I'm going off a first pass through the paper, but it appears that what this paper shows is that the training error can be 0 on an entirely randomized data set, but the generalization error - the difference between the error on the test set and the training set, does increase dramatically as label corruption increases. My understanding is that cross validation does multiple combinations of splitting the input data into test and training sets... so if cross validation measures the generalization error, wouldn't this catch the low predictive value resulting from randomization of labels or input? I'm not saying the paper doesn't have value, but I think it's more about the fact that neural nets can obtain a training error of zero on randomized data, not a testing error (or generalization error, which represents the difference between training error and testing error, as far as I can tell). To be clear, I'm not an expert, and this is just what I gleaned from a first pass over the paper.

screye 8y ago

A good ML system has to be architect-ed to exploit known structure of the task that is being attempted. CNN's exploit spatial locality and LSTMs exploit temporal locality. The SOTA models are architect-ed with even stronger assumptions about the nature of the task. Methods like Neural Networks, Random Forests and SVMs when used as unconstrained universal function approximators for unstructured data only learn some non-linear polynomial/ exponential/logarithmic combination of data itself, without much nuance. It is critical to help a model out by constraining the space of models it searches over to find the right answer. I think, unless we figure out a way to constrain architectures to exploit specific traits of task they are trying to solve, (universal function approximator type) ML won't succeed in the same way that it has in vision / language. As it of now, the alternative is to use PGMs where the model is fully interpretable as a graph structured combination of explicitly parameterized random variables. PGMs work well with low data and give really good uncertainty estimates, to evaluate the quality of a model. PGMs of course suffer from the problem where they are excruciatingly slow for large datasets and require require a decent amount of prior knowledge about the problem to explicitly define the type of graph structure / random variables we are going to be using. I think ML is most certainly capable of solving this problem, but the community is probably waiting for another break through along the lines of AlexNet/LSTMs before that it the case.

deleted 8y ago
[deleted]

pighive 8y ago

Why are we seeing so many stories from bloomberg.com? What tier is bloomberg’s credibility and their sources’? What part of technology, or business is their journalism known for? I am new to US, but I think my questions are not senseless. Thanks.