Comments (3)
Hi @sunxuen ๐
Answers to your 3 questions below.
- Based on the screenshot of the code, you are not using the latest version of "featurewiz". Can you please try upgrading via:
pip install featurewiz --upgrade
- Here is an example of how to use the simple and complex lightGBM models:
https://www.kaggle.com/code/rsesha/netflix-appetency-featurewiz-privatescore-0-78 - Re: transformer of raw data, you need to use the latest syntax of featurewiz:
from featurewiz import FeatureWiz
features = FeatureWiz(corr_limit=0.70, feature_engg='', category_encoders='',
dask_xgboost_flag=False, nrows=None, verbose=2)
X_train_selected = features.fit_transform(X_train, y_train)
X_test_selected = features.transform(X_test)
Hope this helps,
Autovimal
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@AutoViML Thanks your reply, but I still feel confused about third question. Here is my code:
I want to save fitted xgb model for predict raw data, but the return of features.transform(X_test) is unencoded. So I have to write my sklearn pipeline transform for raw data according to data_transforn function?
lastly, Can you check my code for simplifications based on featurewiz?
Thanks!
from featurewiz.
Hi @sunxuen ๐
We have created an easy library to transform your data. First transform it using "lazytransform" on this GitHub site and then use featurewiz. Both of them are sklearn compatible pipelines and you can use them interchangeably.
pip install lazytransform
from featurewiz.
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