Timeline for Random forest regressor accuracy reduces when the input data is not shuffled
Current License: CC BY-SA 4.0
17 events
| when toggle format | what | by | license | comment | |
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| Mar 26, 2022 at 18:36 | vote | accept | lsr729 | ||
| Mar 26, 2022 at 16:03 | history | edited | dipetkov | CC BY-SA 4.0 |
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| Mar 26, 2022 at 12:33 | history | edited | dipetkov | CC BY-SA 4.0 |
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| Mar 26, 2022 at 12:14 | history | edited | dipetkov | CC BY-SA 4.0 |
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| Mar 24, 2022 at 22:55 | comment | added | lsr729 | Let us continue this discussion in chat. | |
| Mar 24, 2022 at 22:40 | comment | added | dipetkov | For some reason it's very upsetting that we got to the bottom of the time series nature of your problem because I asked the right question about it but you accepted the other answer. | |
| Mar 24, 2022 at 21:43 | comment | added | dipetkov | I wasn't clear enough. If you'd like to ask that question, that's fine. Just do in a post of its own and provide all relevant context. | |
| Mar 24, 2022 at 21:38 | comment | added | lsr729 | I am trying to analyze the response variable which is reducing historically. And for that, I have selected 5 explanatory variables that I think are responsible for the behavior of the response variable. I am not going to predict the response variable in the future but would like to do a scenario analysis. | |
| Mar 24, 2022 at 21:36 | comment | added | dipetkov | This is a very different question from the one you actually asked, which describes neither the time series nature of your problem nor what you are hoping to model. | |
| Mar 24, 2022 at 21:32 | comment | added | lsr729 | I am not making this model for the future prediction, but I would like to change the independent data and see how the response variable would have been if the X_1 is increased by 10% ... | |
| Mar 24, 2022 at 21:28 | comment | added | dipetkov | I guess that makes sense as long as you are not planning to use your model for predicting 2022 because then the model is not going to know what happens in 2021 and 2023. | |
| Mar 24, 2022 at 21:21 | comment | added | lsr729 | That's true, b) gives high accuracy and a) gives very poor accuracy. But my understanding is that, if I am providing independent parameters then why the time is important here? | |
| Mar 24, 2022 at 21:14 | comment | added | dipetkov | So here are the two cases you are considering: a) Train the model on data from 1955 to 2010 and check how well it does on 2011 to 2020. b) Give the model a random sample of years and test it on the rest. b) might very well mean that the model "saw" 2010 and 2012 and you ask it to predict 2011. Does b) make any sense to you? | |
| Mar 24, 2022 at 20:59 | comment | added | lsr729 | Thanks, I have posted the plot of my response variable. my confusion is that I am not doing time series analysis. I am providing some independent data and not a time component, and trying to predict a variable. Now, if the response variable is predictable using the independent data, the order of data that is feed should not matter. | |
| Mar 24, 2022 at 17:58 | history | edited | dipetkov | CC BY-SA 4.0 |
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| Mar 24, 2022 at 17:34 | history | edited | dipetkov | CC BY-SA 4.0 |
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| Mar 24, 2022 at 17:28 | history | answered | dipetkov | CC BY-SA 4.0 |