Timeline for Decision tree and random forest over fitting
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 7, 2019 at 16:58 | comment | added | Samarth | Awesome! Hope this thread was helpful. Good luck! | |
| Nov 7, 2019 at 16:32 | comment | added | Mohammad Nur | I was working on 2000 rows data set, i collected 8000 more and now test results are around 0.85 | |
| Nov 6, 2019 at 18:20 | comment | added | Samarth | You can try clustering the countries together based on other features and reduce the number of categories. I would also suggest trying the xgboost model. | |
| Nov 6, 2019 at 18:03 | comment | added | Mohammad Nur | There are more than 100 different county names and they have some information because prices change significantly between different counties and when i excluded them the training result went down. I tried dummy variables but got the same results. | |
| Nov 6, 2019 at 16:27 | comment | added | Samarth | I was trying to suggest something like K Fold cross validation, you can take the mean of all the models rather than taking one. I would suggest using the mean absolute error (MAE) and RMSE to evaluate your results, that would allow you to identify which examples are not doing well on the model. How many country names do you have? Target transform might not be a good approach I would suggest looking at generating dummy variables for regression. Also you should try to visualize the country features and see if they make any sense as a predictor or just drop them. | |
| Nov 6, 2019 at 8:31 | comment | added | Mohammad Nur | 5.The correlation for all features is between 0.5-0.9 except for the variable that resulted from applying target transform to the county variable which its correlation is around 0.1 | |
| Nov 6, 2019 at 8:27 | comment | added | Mohammad Nur | 4.The accuracy metric I used is R^2 | |
| Nov 6, 2019 at 8:27 | comment | added | Mohammad Nur | 3. I used cross_validate from sklearn and got a score around 0.5 but in sklearn there is no way to apply cross_validate on decision tree and pick a final model from it.(as i understood from multiple articles) | |
| Nov 6, 2019 at 5:46 | history | answered | Samarth | CC BY-SA 4.0 |