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I am getting: "Error in xgb.get.handle(model) : invalid xgb.Booster.handle" when using the vetiver_model function on a tuned xgboost model (tune then finalize wf). I don't get the error if I take out…
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Hi Kathrin
for one of my data sets I am setting the parameters as.
```
Namespace(config='config/wheat_anthesis.yml', model_name='XGBoost', dataset='wheat_anthesis', objective='regression',
use…
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I'm entirely new to python and reticulate, so please bare with me, but I think I got most steps working correctly. I am running into two issues:
1 - trying to predict from a trained model,
2 - how d…
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## Descrição da vaga
We are looking for a Senior Data Scientist to analyze large amounts of raw information to find patterns that will help improve our company. We will rely on you to build data pr…
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### pycaret version checks
- [X] I have checked that this issue has not already been reported [here](https://github.com/pycaret/pycaret/issues).
- [X] I have confirmed this bug exists on the [la…
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Classification and regression models often leverage sparse arrays. For example when composing a Sklearn pipeline, sparse arrays are formed as the output of categorical OneHot transforms and ngramming …
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### Week 1 - Get to know the community
- [X] Join the communication channels
- [X] Open a GitHub issue (this one!)
- [X] Install the Ersilia Model Hub and test the simplest model
- [x] Write a motiva…
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Hi, could you provide working example with meaningful comments on how to implement custom loss function? The example included in the tutorials doesn't work for me. It crashes with the following error
…
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Hello,
I'm trying to run regression with distributed XGBoost, but I'm noticing that the results are different when I use distribution vs. when I train with a single process.
I used a custom obje…
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