Timeline for Classification algorithm that only matches trained examples
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Mar 25, 2021 at 14:55 | comment | added | dwkd | Thank you Erwan! We're having great results with XGBoost so far. | |
| Mar 22, 2021 at 19:16 | comment | added | Erwan | ... the right label (the most frequent) for a known instance. A simple decision tree would do that quite well for instance. | |
| Mar 22, 2021 at 19:16 | comment | added | Erwan | @dwkd what you're describing in the comment is a two-stages method: stage 1 is deterministic and stage 2 is statistical. In the case of your problem you can indeed do something similar: 1) if the instance is known then predict the label from the training data (deterministic, as I said in the answer) 2) if the instance is unknown then predict from a model trained from the training set (in this case there might be errors, exactly like a doctor could make a mistake). Note that if the ML setting is chosen to be conservative, it can take care of both (1) and (2) because it would always predict ... | |
| Mar 22, 2021 at 18:59 | comment | added | dwkd | I was expecting this answer but was hoping there's some sort of hybrid deterministic + statistcal algorithm, just as we humans use in the real world. For example, a doctor applies a pure deterministic approach when triaging a patient but quickly starts augmenting it with statistics when new facts that he hasn't encountered before emerge. | |
| Mar 20, 2021 at 11:27 | history | edited | Erwan | CC BY-SA 4.0 |
added 124 characters in body
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| Mar 20, 2021 at 11:19 | history | answered | Erwan | CC BY-SA 4.0 |