Fixed Dummify for satisfying sklearn pipeline structure #1004
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Yard1
requested changes
Jan 16, 2021
pycaret/tests/test_preprocess.py
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| # Append classifier to preprocessing pipeline. Now we have a full prediction pipeline. | ||
| clf = Pipeline(steps=[('preprocessor', pycaret_preprocessor), | ||
| ('classifier', xgb.XGBClassifier())], |
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Can we use something from sklearn instead? Ideally, we would have our own mock classes for all of this, though (though IIRC sklearn provides something like that, doesn't it?)
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You mean like using a classifier from sklearn instead of XGBoost? Yes I can do that. I think as long as the implementation supports the sklearn interface, we can use them in a sklearn pipeline.
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Yeah, I feel that using something like RandomForest will be a better idea, since XGboost may lag behind any API changes
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Thanks for the comment! I have changed XGBoost to random forest.
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