Comments (1)
danielhomola
commented on July 25, 2024
Thanks for the suggestion! Unfortunately, I don't think this is widely applicable or that it be implemented in a way that would work for many datasets.. Quite often those tentative features will be discarded (and rightly so) with subsequent runs, so confirming them due to impatience is the wrong thing to do. Also, if you don't want to wait, simply terminate the run and re-run Boruta with smaller iteration number.
If you do implement this on your fork and find it useful on other datasets, do let me know and then I'll be reconsider a PR for this, but currently I can't see how this feature might be useful for a wide range of datasets.
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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
- Why does the number of total features (Confirmed + Tentative + Rejected) not equal to the input features?
- Does BorutaPy work with cuML RandomForestClassifier? HOT 8
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