Timeline for Quantile Regression followed by classification
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Oct 23, 2019 at 14:39 | answer | added | Econometrics_Wonk | timeline score: 2 | |
| Jan 12, 2018 at 21:25 | comment | added | Chandra | @Jon, thank you for responding. My question does pertain to feature selection. It is regarding predicting different quantile values. I am seeking answer to identify whether predicting median after classification is same as predicting different quantiles using quantile regression. | |
| Jan 12, 2018 at 21:21 | comment | added | Jon | If you've checked VIF's, then I assume you've at least run 1 linear regression model. If you did, you probably saw the coefficients and p-values. I would inspect those with low effect sizes and/or high p-values to see if there is any significant relationship. If not, drop them. Model building is not an easy process; it's tedious work. | |
| Jan 12, 2018 at 20:31 | comment | added | Chandra | I have checked VIF and removed highly correlated features. 110 columns is after doing all necessary checks. | |
| Jan 12, 2018 at 20:22 | comment | added | Jon | Have you tried doing some basic exploratory data analysis? Surely, there are a few variables that can be immediately dropped out from that process. I mean, you can at least look at some simple correlations. | |
| Jan 12, 2018 at 19:32 | history | asked | Chandra | CC BY-SA 3.0 |