Timeline for How regression algorithm works on categorical features
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jan 3, 2018 at 14:14 | comment | added | Reza | Yeah, thanks, actually the original issue is performance (r2) on my data is very low (0.20) so I am trying to find root cause and I tought categorical features could be root cause | |
| Jan 3, 2018 at 10:09 | comment | added | vc_dim | Yes it will be a straight line. But the point is if it will have a slope. That slope will help you get a good regression. The Chart you have at the end has a nice slope. But if I try to do a similar trendline for Top two charts, I might not be getting a nice slope as data is scattered all over the place instead of showing a good pattern. Changing encoding will not help there. Let me know if you need more detail. I can edit my answers to include some examples. | |
| Jan 1, 2018 at 16:40 | comment | added | Reza | I don't get it, consider there is only one categorical feature with values A,B, if you do one-hot encoding the values for column-A will be 0 and 1 and same for column-B, now if you do a scatter based of label and column-A, it will be some straight lines, correct? | |
| Jan 1, 2018 at 14:59 | comment | added | vc_dim | It won't, hot encode is just a representation of the data. So if your data itself does not have linear pattern with the value, no encoding will show a pattern, hence a bad R-square value. | |
| Dec 31, 2017 at 16:41 | comment | added | Reza | Thanks for answer, SGPriority has 3 values 'Critical,Important,Trivial,' so I convert it to 3 numbers 7,5,3 and the I used normalization which generates these 3 new numbers, the question is either with normalization or without or just one-hot encoding categories if I draw a scatter plotter between the feature and lablel it will be something like that, because there will be always 2 (or multiple) concrete values, I don't know hot could it get to something that you drew | |
| Dec 31, 2017 at 6:13 | history | answered | vc_dim | CC BY-SA 3.0 |