Comments (1)
danielhomola
commented on June 26, 2024
3
more features than it should
How do you know what it should do? Seems like you have strong priors when looking at your data.
to have it working properly?
Boruta is a published FS method, used by thousands of people in different fields and competitions. It is working properly, as in, it's doing what it says on the tin (have you read the paper?)
None of your comment is quantitative, benchmarked or scientifically helpful in any sense, so I'll recommend you read the paper, or at least the readme (then you'd know, adjusting the p-value cut-off helps).
Closing the thread.
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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
- importance_history_ HOT 2
- ENH: Add option for minimum number of confirmed features
- Any updates in future ?
- Does BorutaPy work with cuML RandomForestClassifier? HOT 8
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