Timeline for How to make a decision tree with both continuous and categorical variables in the dataset?
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
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| Feb 6, 2021 at 9:37 | history | edited | David Masip | CC BY-SA 4.0 |
Improve implementation
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| Jun 15, 2018 at 19:42 | comment | added | Sahil Chaturvedi | Yes, that was pretty much helpful @DavidMasip. I actually had confusion regarding particulary continuous variables and it got cleared now :) | |
| Jun 15, 2018 at 19:41 | vote | accept | Sahil Chaturvedi | ||
| Jun 4, 2018 at 19:42 | history | edited | David Masip | CC BY-SA 4.0 |
added 607 characters in body
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| Jun 4, 2018 at 18:39 | comment | added | Jakub Bartczuk | It really depends on algorithm. For example decision trees used in popular Python packages (scikit-learn and XGBoost) can't handle categorical data out of the box (scikit-learn for example uses CART algorithm) | |
| Jun 4, 2018 at 18:36 | history | answered | David Masip | CC BY-SA 4.0 |