EpistasisLab/scikit-rebate
A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.
PythonMIT
Issues
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no module found
#82 opened by gjj07 - 0
Discrepancy between documentation(discrete_limit) and github code(discrete_threshold)
#80 opened by jckkvs - 2
Refactor: Looking for implementation strategies to improve run time efficiency of all algorithms regardless of data type (i.e. discrete/continuous, missing data)
#39 opened by ryanurbs - 1
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Have the implementation of reliefF weighted with the prior probability of each class?
#78 opened by dahaiyu - 2
- 7
Questions about sample code?
#57 opened by megancooper - 2
Weights are different for different runs
#76 opened by moumitam28 - 1
VLSRelief + MultiSURF run time
#74 opened by brunofacca - 0
Progress bar for VLSRelief
#75 opened by brunofacca - 2
DeprecationWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib
#65 opened by raomidi - 3
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ModuleNotFoundError: No module named 'skrebate'
#59 opened by k8iechen - 1
How to use this package without sklearn?
#56 opened by l0o0 - 8
TuRF value error
#52 opened by J-Bleker - 1
TuRF doesn't work with odd number of features
#54 opened by jgoecks - 0
Add sklearn.utils.check_array to fit and predict
#53 opened by mpearmain - 7
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Extend the unit tests
#11 opened by rhiever - 1
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Code cleanup
#10 opened by rhiever - 0
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Add package documentation
#4 opened by rhiever - 0
Add unit tests
#3 opened by rhiever - 0
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