Timeline for Cost sensitive learning and class balancing
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Jul 20, 2020 at 8:55 | comment | added | A1010 | ook thank you. I will check if you will implement an example. it will be really usefull | |
| Jul 18, 2020 at 8:20 | comment | added | German C M | Maybe you could also try to treat those class weights as hyperparameters which you can tune with bayesian hyperparametrization, while still considering the cost based on FN & FP your final evaluation metric (I will try to implement an example of this in case I see it feasible) | |
| Jul 16, 2020 at 12:51 | comment | added | A1010 | So you will use the weights after the training step, right? Originally, I would like to insert the weights directly into the training, modifying the loss function according to a value w that is class-specific. | |
| Jul 16, 2020 at 7:41 | comment | added | German C M | I would focus on the main aim, which is providing that ranking, but knowing that the classification is good enough for not contacting the wrong users. I would first try to build a robust enough classifier (by over/under sampling for instancwe), and then I would try to optimize the class weights to get the highest probabilities for that top 10% of the users in your ranking. | |
| Jul 15, 2020 at 17:30 | comment | added | A1010 | thank you! I am not interested in putting a 0/1 label for each record. Using the output score, I sort the test dataset and I cut the record at a specific percentile (let's say 10% of the test dataset). The records that fall into the first 10% will be 1, the others 0. Do you believe that in this scenario the class balancing and the cost-sensitive learning could improve the performance? Basically, I am not interested in the specific score, but just in the ranking | |
| Jul 15, 2020 at 16:13 | history | answered | German C M | CC BY-SA 4.0 |