Timeline for OpenCV parameters of Random Trees
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 5, 2013 at 6:19 | comment | added | stefangachter | Meanwhile I re-read the random forests chapter of Hastie's et al "The Elements of Statistical Learning", which confirms the above statements. | |
| Aug 2, 2013 at 5:01 | comment | added | stefangachter | Thanks for the clarification. If I understood you correctly, maximum depth is one parameter to further optimize the classifier in certain cases, but other parameters bear greater weight, e.g. the number of trees. | |
| Aug 2, 2013 at 3:25 | comment | added | JEquihua | There are no established rules to choose optimal depth. Generally, Random forests work well when grown to full depth. Especially in classification. By default, the vanilla implementation in R grows trees until there are 5 obs. in the final nodes for regression and 1 for classification. From Experience I have seen an improvement for regression by growing the trees to maximum depth for some data sets. For some classification problems, especially if the classes are very imbalanced; setting larger final node sizes sometimes helps. Really, you have to play around with your specific problem. | |
| Aug 1, 2013 at 10:15 | vote | accept | stefangachter | ||
| Aug 1, 2013 at 10:15 | comment | added | stefangachter | thanks for the information. i have two follow up questions: is this relevant to regression or to classification as well? is there a rule or procedure how to choose the optimal depth? | |
| Aug 1, 2013 at 9:38 | history | answered | seanv507 | CC BY-SA 3.0 |