#1
Posted 10 October 2023 - 03:23 AM
- Albertsnarm, Natalinak, DanielGon and 25 others like this
#4
Posted 11 October 2023 - 01:06 AM
Thank you for your suggestion. I have tried looking at “Core Output,” but unfortunately, there were hundreds of thousands of lines to read, making it very difficult to find the information I need. I appreciate your help nonetheless.
Hi Crystal,
Have you tried looking at "Core Output." This can be found in Results Tab >> Text Output >> Core Output.
Thanks
Mouli
- Crystal likes this
#5
Posted 11 October 2023 - 01:13 AM
Thank you very much for your assistance; I have found the information you mentioned. However, I still have a question regarding the results. I noticed that the success rate appears to be consistently 100% when performing bootstraps in Phoenix. Is it genuinely possible to achieve a 100% success rate in bootstrap runs?
Hi Crystal,
you can also inspect the BootOverall sheet which lists the detailed outcome from each bootstrap run:
The number of rows in that table divided by the number of replicates will give you the success rate.
Bernd
- Crystal likes this
#6
Posted 11 October 2023 - 08:14 AM
Thank you very much for your assistance; I have found the information you mentioned. However, I still have a question regarding the results. I noticed that the success rate appears to be consistently 100% when performing bootstraps in Phoenix. Is it genuinely possible to achieve a 100% success rate in bootstrap runs?
The success rate depends on a couple of requirements:
1. a robust engine - with FOCE_ELS and QRPEM, Phoenix got two very strong and robust engines
2. a model that is representative of the whole training set, so that every re-combination of a training set that form a new bootstrap sample will converge
You might have been lucky to see a 100% success rate consistently, but eventually you will encounter a situation where a particular combination of data set and model will not achieve that maximum rate.
Bernd
#7
Posted 12 October 2023 - 07:32 AM
Thank you so much, I really appreciate it.
The success rate depends on a couple of requirements:
1. a robust engine - with FOCE_ELS and QRPEM, Phoenix got two very strong and robust engines
2. a model that is representative of the whole training set, so that every re-combination of a training set that form a new bootstrap sample will converge
You might have been lucky to see a 100% success rate consistently, but eventually you will encounter a situation where a particular combination of data set and model will not achieve that maximum rate.
Bernd
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