Timeline for Is the output of all kinds of machine learning models binary or can the end values be interpreted in other ways?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Nov 24, 2024 at 5:08 | vote | accept | user81993 | ||
| Nov 2, 2024 at 19:14 | answer | added | Robert Long | timeline score: 3 | |
| Mar 23, 2022 at 14:00 | comment | added | amon | NNs generally output a vector of floats that can be interpreted as probabilities. You can go further and round them up/down to 0/1 to make decisions, but that's not always appropriate. E.g. for a labelling problem you'd have one output neuron per label, and then might display the labels with the top three probabilities. You associate the output neurons with a particular meaning via training. If neuron #3 means “cat found” and you train the NN on a cat image and the neuron doesn't output 1.0, you'd back-propagate the error in order to update weights. 10k params is large for a toy model though. | |
| Mar 22, 2022 at 10:08 | comment | added | Stack Exchange Broke The Law | What is the output supposed to represent? They aren't just numbers that are there for no reason! What is the network trying to learn? | |
| Mar 22, 2022 at 8:52 | comment | added | IMSoP | FYI, there is also an Artificial Intelligence Stack Exchange where you might get more in-depth answers (but would need to make sure your question met their standards). A couple of relevant questions from a quick search: ai.stackexchange.com/q/7911/34038 and ai.stackexchange.com/q/28406/34038 | |
| Mar 22, 2022 at 1:38 | history | asked | user81993 | CC BY-SA 4.0 |