Timeline for The accuracy of a random forest algorithm is nearly 1, how do I solve this problem? (with updates)
Current License: CC BY-SA 4.0
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| when toggle format | what | by | license | comment | |
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| Sep 4 at 12:53 | answer | added | Ben Reiniger♦ | timeline score: 1 | |
| Sep 4 at 8:28 | comment | added | Philoner | @BenReiniger, thank you for you answer, I thought about the fact that maybe the data are too esay to predict, but how do I understand that this is the truth instead of an overfitting problem? | |
| Sep 4 at 8:22 | history | edited | Philoner | CC BY-SA 4.0 |
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| Sep 4 at 8:15 | history | edited | Philoner | CC BY-SA 4.0 |
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| Sep 4 at 1:41 | comment | added | Ben Reiniger♦ | I thought we had a relatively general thread about identifying what's reasonable vs. what's too good to be true, but the closest I can find now is the more-specific datascience.stackexchange.com/q/84567/55122 | |
| Sep 4 at 1:37 | comment | added | Ben Reiniger♦ | Are you sure there is a problem? Perhaps the data is just relatively easy to predict? | |
| Sep 4 at 1:04 | answer | added | user167433 | timeline score: 2 | |
| Sep 3 at 21:03 | history | edited | Philoner | CC BY-SA 4.0 |
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| Sep 3 at 21:02 | history | edited | Philoner | CC BY-SA 4.0 |
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| Sep 3 at 21:00 | history | edited | Philoner | CC BY-SA 4.0 |
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| S Sep 3 at 20:57 | review | First questions | |||
| Sep 4 at 7:08 | |||||
| S Sep 3 at 20:57 | history | asked | Philoner | CC BY-SA 4.0 |