3 ms·

Sexy is in the eye of the beholder. Seriously, I think the problem of ML is that it often goes against the so-called principle parsimony, which plays a critica

by lottin 10y ago

Sexy is in the eye of the beholder. Seriously, I think the problem of ML is that it often goes against the so-called principle parsimony, which plays a critical role in statistical modelling. Especially in many areas where the whole point isn't strictly to predict future outcomes, but to understand how things work and to test hypotheses. Understanding leads to better prediction anyways. If you can predict something but you don't know why or how, I would take such predictions with a grain of salt.

jackcosgrove 10y ago
I think you have the cart before the horse regarding understanding and prediction. Prediction precedes understanding in importance and sometimes methodology, since if you can't predict outcomes correctly your understanding, however well fleshed-out, will be inaccurate. Interestingly, the parsimony principle is lent empirical weight by bad results obtained from overfitting. Machine learning enables us to multiply many more entities than we can using our conscious thought processes. The various image recognition models use many variables that enable results impossible with other techniques. It does not violate parsimony if you can obtain better results on a large data set. I can predict that if a car drives by it will not teleport to some other location, but will rather continue along its path. I've been able to predict this since I was a child. I did not need to study the laws of motion to do so. I would not take such a prediction with a grain of salt.
- kgwgk 10y ago
  In many cases, an acceptable prediction plus understanding is better than a more accurate prediction alone. And I'd say the example in your last paragraph is an example of understanding, by the way, or you would not have been able to express it in abstract terms. (For a young child, an animal or any other simple mind it may be possible to get the prediction without understanding, I guess).
randcraw 10y ago
I think that's because the majority of folks using ML care only about discriminative ML, not generative. A black box ML technique that delivers low predictive errors is good enough. To those ends, parsimony is largely irrelevant. I suspect that others using ML, who are inclined to test hypotheses, induce cause, and discover unknowns (via generative ML), are far fewer. So it's hardly surprising that the eye of discriminative MLers dominates in defining 'sexiness'.

by lottin 10y ago

jackcosgrove 10y ago

I think you have the cart before the horse regarding understanding and prediction. Prediction precedes understanding in importance and sometimes methodology, since if you can't predict outcomes correctly your understanding, however well fleshed-out, will be inaccurate. Interestingly, the parsimony principle is lent empirical weight by bad results obtained from overfitting. Machine learning enables us to multiply many more entities than we can using our conscious thought processes. The various image recognition models use many variables that enable results impossible with other techniques. It does not violate parsimony if you can obtain better results on a large data set. I can predict that if a car drives by it will not teleport to some other location, but will rather continue along its path. I've been able to predict this since I was a child. I did not need to study the laws of motion to do so. I would not take such a prediction with a grain of salt.

kgwgk 10y ago
In many cases, an acceptable prediction plus understanding is better than a more accurate prediction alone. And I'd say the example in your last paragraph is an example of understanding, by the way, or you would not have been able to express it in abstract terms. (For a young child, an animal or any other simple mind it may be possible to get the prediction without understanding, I guess).

randcraw 10y ago

I think that's because the majority of folks using ML care only about discriminative ML, not generative. A black box ML technique that delivers low predictive errors is good enough. To those ends, parsimony is largely irrelevant. I suspect that others using ML, who are inclined to test hypotheses, induce cause, and discover unknowns (via generative ML), are far fewer. So it's hardly surprising that the eye of discriminative MLers dominates in defining 'sexiness'.