xgboost-distribution
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CDonnerer
Probabilistic prediction with XGBoost.
Hi! First and foremost, I would like to congratulate you on the very much anticipated features that you have implemented in this repo! Have you thought of talking with the...
Hi, I have used XGBDistribution model trained by me for very long time, before it was all fine, but recently something strange happened. When I call model.predict(), it give me...
When I run the following code snipper I get an error "numpy.linalg.LinAlgError: Singular matrix" `X_train, y_train, X_test, y_test = read_data(InEx) model = XGBDistribution(distribution="normal", n_estimators=500) model.fit(X_train, y_train, eval_set=[(X_test, y_test)], early_stopping_rounds=10)` The...
I love the xgboost distribution package and what it enables, however when dealing with datasets or trees that do not fit into memory one needs to scale the task using...
Hi and thanks for a great package! I have two small questions. 1) What would it require to implement a beta-distribution to fit fractions or probabilities? 2) I see that...
Hi @CDonnerer, Thanks for a great package! I am bit in doubt about how to contribute to your package; I have e.g. never used tox before. As such, I couldn't...
Hello - thanks again for the wonderful package. I wanted to ask whether it would make sense to adjust the current `.predict` API to mimic NGBoost's performance, of returning point...
Xgboost supports categorical values directly, without going through a one-hot sparse array. In the example below, I specify the `a` series to be a categorical int type. Then I pass...
In the docs their is a demonstration of using monotono constrains (which is a nice feature in XGBoost too). Is it possible to only constrain `mean` and not `std`? I...
