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Hi, thats a cool thesis idea. You probably want to look at LightGBM with linear weak learner and KTBoost, SnapBoost, [PolyBoost](https://github.com/SaremS/polynomial_boosting) as well.
Just lookin…
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Thanks for this repo!
I wonder if it is possible to extend the `BoostingRegressor` to work with the data where censoring points `yl`, `yu` are varied by observations.
kota7 updated
3 years ago
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Suppose you have experimental activity data, say pIC50's at some target for a set of compounds, but the experiment was only able to measure activity in the interval [5, 9.5], below and above which the…
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Hi, KTBoost seems to have problem with the wrong dependency on scikit-learn again? Both the Regressor and Classifier.
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I am using KTBoost 0.1.19 and got the following error:
Traceback (most recent call last):
File "train_model.py", line 135, in
ktb_regressor.fit(X_train_cv, y_train_cv)
File "C:\Prog…
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In the negative gradient of the tobit loss function, residual accounts for the sample_weight for each observation. In addition to that, sample weight is being accounted for again in the leaf update st…
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Hello,
I did some bench w/ the 3 criterions available for classification, and mae is at least one order of magnitude slower. any reason?
```
model = KTBoost.BoostingClassifier(loss='deviance',u…
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I get the following error when trying to apply a Grabit model with KTBoost.BoostingRegressor(loss='tobit', yl=0, yu=1).fit(X, Y):
Traceback (most recent call last):
File "", line 2, in
File …
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I think there might be some change required for update_terminal_regions method in LossFunction for If condition. We might need to change this to
if update_step == 'hybrid' or update_step == 'grad…