Comments (5)
@AutoViML: The new version doesnt remove these category columns. but its process seems diffrent from the old version
1- Because of Ram my PC crashed (I have 64 G Ram + 270 G Swap) but old one doesnt have this problem.
2-The new version selected 3600 features but the old version selected 2700 and the old one's selection F1 is higher.
I can open a ticket for that.
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Hi @reza1615 : Can you upgrade your featurewiz library to the latest version and try? I believe seeing your output that you are using a older version. I will update the library if you see any problems with the latest version. thanks
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Hi @reza1615
After looking at your problem, some more, I realized that SULOV is meant only for finding correlation among numerical vars 👍
Hence, I skip sulov processing for categorical vars. But I absolutely include them during XGBoost feature selection. See my code snippet from featurewiz/FE_perform_recursive_xgboost
below:
dtrain = xgb.DMatrix(X_train, y_train, enable_categorical=True, feature_names=cols_sel)
Can you please double-check this is working for you?
Thanks
Auto Vimal
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Thank you for your time. I will update featurewiz base on the repo and also skip the SULOV to see what will happen
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@reza1615 👍 I suggest you stick to the old version if it works better. I have made so many changes to the new version, it will be difficult to find out what was different. You can post the version numbers of the old and new here so I can take a look when I have some time.
Auto Vimal
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Related Issues (20)
- Category type, indexes don't match on AutoEncoding HOT 3
- Issue with working with Featureviz HOT 1
- Comment has incorrect code ( verbose=0. imbalanced=False [verbose=0, imbalanced=False]) HOT 1
- make tensorflow optional HOT 4
- lazytransform.py float to integer error HOT 2
- Dealing with a Numpy array as features HOT 1
- Convert binary columns to categorical HOT 1
- featurewiz ignores category columns HOT 2
- dont show chart for more than 1000 features HOT 1
- Universal API required for smooth working HOT 4
- wiz.tarnsform returns the same data as the train for test HOT 1
- package versions have conflicting dependencies HOT 1
- TypeError: expected string or bytes-like object on int type column name HOT 2
- Conflict Error Among Poetry Package Dependencies: lazytransform, tqdm, featurewiz HOT 8
- TYPO ERROR
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- Version Conflict for scikit-learn - Bump to 1.3.2 possible? HOT 1
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- ValueError: Length mismatch: Expected axis has X elements, new values have Y elements HOT 3
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