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Reference: https://www.kaggle.com/general/17120
* using python:
http://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
* using R:
http://www.a…
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### Describe the workflow you want to enable
The histogram gradient boosted decision trees usually do not fulfil the so called *balance property* on the training data, i.e. `sum([proba]predictions) =…
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# Main Remark
Tabnet architecture is using sequential steps in order to mimic some kind of random forest paradigm.
But since boosting algorithms often outperform random forests shouldn't we try to…
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The Criteo dataset is a 1TB dump of features around advertisements and whether or not someone clicked on the ad. It has a both dense and categorical/sparse data. I believe that the data is freely av…
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Hello,
I have the following problem: I noticed that when using the new version (1.2.0) of Tensorflow Decision Forests, if I pickle a trained model (gradient boosted trees), then load it and run the…
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```
There are two issues:
1) setting the seed does not ensure reproducibility of the model
2) when no predictors are used in any splits, the predicted values are somewhat
inconsistent; sometimes NA …
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Minor detail that has been bothering me:
- `plasma-python` GitHub repository name
- `plasma` Python module name
- `PPPLDeepLearning` GitHub Organization name
- "PPPL deep learning disruption predi…
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The parameters **subsample** and **max_features** in GradientBoostingRegressor are useful. Is it possible to add equivalent parameters to HistGradientBoostingRegressor?
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Hi folks,
I stuck on this topic for couple days and seems I need your help to understand what is expected value in TreeExplainer when feature_perturbation = tree_path_dependent, precisely what is e…
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### Problem Description
I'm currently working on a model (torch), that takes a dict of tensors as input. Thing is, each tensor has a wildly different shape, so trying to use an np array as the input …