Timeline for Is there a possibility that there is no relationship between some inputs and outputs?
Current License: CC BY-SA 4.0
12 events
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| Mar 4, 2020 at 18:46 | comment | added | k.c. sayz 'k.c sayz' | actually i've came to realize that you've asked the same question here: datascience.stackexchange.com/questions/69083/… , which is a better place to ask that question anyhow. @Dave 's answer there pretty much superseeds mine and is as clear as it gets | |
| Mar 4, 2020 at 18:41 | history | edited | k.c. sayz 'k.c sayz' | CC BY-SA 4.0 |
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| Mar 4, 2020 at 17:52 | comment | added | k.c. sayz 'k.c sayz' | you might be interested in this: stats.stackexchange.com/questions/320375/… if you've read that, then the "formal interpretation" of what i meant was that the training/validation set comes from a different "data-generating distribution". if you've read this far you might come to the suspicion that i am again being pedantic by providing an answer like this. but i'll add something to my answer that might address your original intent of the question. | |
| Mar 4, 2020 at 17:48 | comment | added | k.c. sayz 'k.c sayz' | last paragraph tl;dr: if your validation/training set are "obviously unrelated" then you might argue that you cannot learn any relationship between them. but the term "obviously unrelated" is loaded with tricky epistemic (as in: philosophical) issues, which makes it non-trivial to unpack, which is why i gave the example as above. | |
| Mar 4, 2020 at 17:44 | comment | added | k.c. sayz 'k.c sayz' | ...machine learning is literally math/stats......... you asked if something might happen in theory, so i answered your question "as in theory", which is why i gave you an answer this is "technically correct" | |
| Mar 4, 2020 at 17:35 | comment | added | basilisk | my question clearly shows that I'm asking from a machine learning aspects and not just mathematics. Please read the question again, I stated whether mL model would fail sometimes to find pattern in data because of randomness. you think I lack understanding, I would say who doesnt? . Moreover, I think everyone knows that data can be memorized with if then.. but I'm asking for a relationship, a pattern, a mapping function.. I don't know what to say more. also I didn't understand the last paragraph you wrote, can you clear more | |
| Mar 4, 2020 at 13:57 | comment | added | k.c. sayz 'k.c sayz' | and in any case, in the last paragraph i already answered your question regarding separate validation/training sets | |
| Mar 4, 2020 at 13:56 | comment | added | k.c. sayz 'k.c sayz' | >"a relationship between data doesn't mean [...]" yes it does. both a "usual ML function" and an "if then statement" are functions that map from input space to output space. if you don't acknowledge this, then you lack understanding. your initial question asks for a theoretical result regarding a /fixed dataset/, and hence i was able to cheese an answer by suggesting you can always achieve your intended result by overfitting. now, your intention seems be about learning the underlying "data generating distribution", which is very different from how you initially posed the question | |
| Mar 4, 2020 at 13:46 | comment | added | basilisk | a relationship between data doesnt mean memorizing the values with an if then statements! a relationship is a function that can be created to map input x to output y. Furthermore, of course you can use gradient descent in classification, you need to choose the right differentiable loss function for it (at least in a certrain range). Your answer does not answer my question. as you used in the first paragraph, using if then will give you 100% accuracy on training data but who validate on trainingdata?for capturing a pattern you need to test on test data, in your case the model giv0% on test data | |
| Mar 4, 2020 at 13:38 | comment | added | k.c. sayz 'k.c sayz' | @basilisk strictly speaking, an if....then.. algorithm is as valid of a classification algorithm as any other, the main issue being that it is not differentiable, and thus you can't run things like gradient descent on it, and what i said applies for an overtrained model anyhow. >"is there a dataset [...] no model here that can find relationship in this data" i have already answered your question, but to spell it out for you: strictly speaking, likely no, unless your training/validation set pair was poorly picked, and you can always overfit your set if you don't have a training/validation pair. | |
| Mar 4, 2020 at 7:32 | comment | added | basilisk | thanks for the answer, my goal is not to make an if. if I used machine learning model with a validation and test set the model would overfitt if i made if else all the time!! by relationship I mean a pattern that map such data to an output. precisely, is there a dataset where i can look and say no sorry there is no model here that can find relationship in this data, hence, machine learning can do nothing for me in this case | |
| Mar 4, 2020 at 4:16 | history | answered | k.c. sayz 'k.c sayz' | CC BY-SA 4.0 |