All Questions
6 questions
1
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397
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Optuina pruning during CrossValidation, does it make sense?
I'm currently trying to build a model using CatBoost. For my parameter tuning, I'm using optuna and cross-validation and pruning the trial checking on the intermediate cross-validation scores. Here ...
1
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0
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151
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Catboost not working properly when I remove non important variables (source of randomness?)
I was wondering if anyone has encountered the same. The thing is, when I run a catboost boosting model, delete non important variables (feature importance by prediction importance = 0, in fact these ...
- 75
1
vote
1
answer
250
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Model Dump Parser (like XGBFI) for LightGBM and CatBoost
Currently my employer has multiple GLM in a live environment. I am interested in identifying new features and interactions to enhance the accuracy of these GLM; for now I am limited to the GLM ...
- 1,665
0
votes
1
answer
537
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Why does Catboost outperform other boosting algorithms?
I have noticed while working with multiple datasets that catboost with its default parameters tends to outperform lightgbm or xgboost with its default parameters even on a tabular dataset with no ...
- 153
2
votes
1
answer
716
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Catboost multiclassification evaluation metric: Kappa & WKappa
I am working on an unbalanced classification problem and i want to use Kappa as my evaluation metric. Considering the classifier accepts weights (which i have given it), should i still be using ...
- 31
0
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1
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575
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training gradient boosting algorithm in python testing in Golang
What are the best strategy to train and save a gradient boosting algorithm, e.g. LightGBM or XGboost or Catboost in Python but load the model in GoLang and make prediction with Golang ?
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