Timeline for Predictive power CART pruned vs. unpruned tree
Current License: CC BY-SA 3.0
16 events
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| Apr 11, 2020 at 13:03 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Dec 12, 2019 at 21:11 | comment | added | EngrStudent | CART is a weak learner. Why not use a random forest? It can substantially outperform, and it is robust so noise has a smaller than expected impact to prediction. | |
| Dec 12, 2019 at 20:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Nov 12, 2019 at 1:20 | history | edited | kjetil b halvorsen♦ |
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| Oct 31, 2019 at 11:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Oct 1, 2019 at 5:30 | answer | added | rapaio | timeline score: 1 | |
| Oct 1, 2019 at 5:01 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| May 20, 2019 at 3:02 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Jan 16, 2019 at 2:00 | history | bumped | CommunityBot | This question has answers that may be good or bad; the system has marked it active so that they can be reviewed. | |
| Sep 1, 2016 at 11:48 | answer | added | USER_1 | timeline score: 0 | |
| May 23, 2016 at 17:07 | comment | added | ShainaR | make sure all the settings are right for a regression tree | |
| May 23, 2016 at 9:42 | comment | added | Antoine | What you observe could be caused by many things. Without further information it will be tough to identify the issue. I think it would help if you provided a minimal reproducible example (small sample of your data and code). | |
| May 23, 2016 at 9:34 | comment | added | sebsch88 | @Antoine MAPE is the Mean Absolute Percentage Error (en.wikipedia.org/wiki/Mean_absolute_percentage_error). The dataset consists of 1.4 million entries. I consider 13 predictors which are categorical and have between 5 and 361 values. As the predicted variable is continous (sales price) I obviously use a regression tree. | |
| May 23, 2016 at 9:23 | comment | added | Antoine | what is MAPE? what is the size of your data set, how many predictors do you consider? of which type? | |
| May 23, 2016 at 9:14 | review | First posts | |||
| May 23, 2016 at 9:23 | |||||
| May 23, 2016 at 9:13 | history | asked | sebsch88 | CC BY-SA 3.0 |