Comments (2)
hatunina
commented on June 21, 2024
Hi @JIE-YUAN ,
I also had the same question.
I solved it by using lgb scikit-learn api .
Because Boruta uses "feature_importances_" internally.
However, it may not be wrong because it is not used as much yet. Sorry at that time.
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marcospiau
commented on June 21, 2024
I managed to get lgb working by following the instructions on the comments section of this kernel on Kaggle (https://www.kaggle.com/ogrellier/noise-analysis-of-porto-seguro-s-features). You need to change the line 282 of boruta_py.py to:
self.estimator.set_params(random_state=self.random_state.get_state()[2])
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Related Issues (20)
- Numpy types aliases deprecated (`np.int`, `np.bool` and `np.float`)
- why estimators num is calculated by feature num in this way?
- max_iter values HOT 3
- ImportError: cannot import name 'BorutaPy' from 'boruta' HOT 1
- PKG for the survival analysis HOT 6
- Can I somehow speed the Borutapy process HOT 2
- Version update of Boruta on pypi? HOT 5
- What percentage of shadow features does each real feature outperform?
- AttributeError: module 'numpy' has no attribute 'int'. HOT 9
- Possible problems in installation HOT 1
- TypeError: BorutaPy.__init__() got an unexpected keyword argument 'early_stopping' HOT 1
- Kaggle n_estimators issue with DecisionTreeClassifier HOT 2
- Error when using BorutaPy with LogisticRegression
- AttributeError: module 'numpy' has no attribute 'bool' when using BorutaPy with RandomForestClassifier HOT 3
- BorutaPy selects different features in different iterations HOT 1
- AttributeError: module 'numpy' has no attribute 'int'. `np.int` was a deprecated alias for the builtin `int`. HOT 13
- Does boruta apply to time series data? HOT 1
- New release HOT 1
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