Timeline for how to know what my data are uninformative and that machine learning will not work with it?
Current License: CC BY-SA 4.0
3 events
| when toggle format | what | by | license | comment | |
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| Dec 19, 2019 at 11:11 | comment | added | Blenz | He needs to look deeper for places where there is indeed correlation, because on average as it appears there is none from which the model can predict the target. | |
| Dec 19, 2019 at 11:10 | comment | added | Blenz | Sure without correlation there can be causation. But in the example given, the interaction is causing a near zero correlation which is what the model will pick up. It doesn't matter if on a straight surface, speed increases by stepping harder on the pedal. If you feed to a model only data about hills, it won't pick up the natural speed/pedal relationship so it clearly depends on the data not on 'commong knowledge' and that's why you need to look at correlation matrices when doing EDA and not counting on what we know as humans. | |
| Dec 19, 2019 at 10:57 | history | answered | Noah Weber | CC BY-SA 4.0 |