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This is currently broken in the latest GitHub versions, which are required since CRAN versions of mlr3 and mlr3mbo are not compatible if I remember correctly, as I'm getting:
```
Learner (surv.xgb…
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1. Feature selection via exhaustive search.
2. Estimate search time
3. Try several other linear (svm) and non-linear (random forest, extra tree, gradientboost, xgboost) model
4. Model interpretation …
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Total Points: 7/8
Title & Abstract (0.5/1)
The language you use to describe your approach is incredibly vague and the tone is too informal. Try to be more precise in your language when defining …
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`{xgboost}` can fit boosted, penalized linear models by setting `booster="gblinear"`. This would be a great addition to `linear_reg()`, or perhaps there can be a `boost_linear_reg()` function and it c…
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The package should be successfully installed and works on any python environment. Including the common ones:
- Jupyter
- JupyterLab
- Conda
- Kaggle
- Google Colab
- Binder
- Amazon SageMaker…
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Why is predicting the leaf **way** slower than actual predicting? Doesn't xgboost have to get the leaf anyway for the predicted value?
```python
X, y = make_regression(n_samples = 100000, n_featur…
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Context from @StrikerRUS:
>>>
Now Go is failing (refer to https://github.com/BayesWitnesses/m2cgen/pull/200#issuecomment-624063683):
```
=================================== FAILURES ==============…
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Some models allow infinite dimensional hyper-parameters.
**Eg 1**: [XGBoost](https://juliacomputing.com/blog/2020/02/24/ad-xgboost.html) allows custom loss functions. (As does @xiaodaigh's [JLBoost.…
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Due to the simplicity of BIC and from experience it seems there's always a nice minimum. In terms of basis optimisation I think it may make sense to start from low polynomial degree and increase such …