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@tswast tswast commented Sep 15, 2025

This is in alignment with https://xgboost.readthedocs.io/en/stable/python/python_api.html\#xgboost.XGBRegressor if considering BQML to be a booster type.

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Fixes internal customer request 🦕

@tswast tswast requested review from a team as code owners September 15, 2025 22:11
@tswast tswast requested a review from GarrettWu September 15, 2025 22:11
@product-auto-label product-auto-label bot added the size: m Pull request size is medium. label Sep 15, 2025
@product-auto-label product-auto-label bot added the api: bigquery Issues related to the googleapis/python-bigquery-dataframes API. label Sep 15, 2025
"enable_global_explain": self.enable_global_explain,
"xgboost_version": self.xgboost_version,
}
options.update(self._extra_bqml_options)
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@GarrettWu GarrettWu Sep 16, 2025

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It may override existing options, if user pass in both.

We use sklearn terms instead of BQML terms for existing parameters, e.g. learning_rate instead of LEARN_RATE. User may get confused looking at two sets of options at the same time.

It becomes hard for us to both stick with sklearn-like experience and support BQML other offerings.

Or maybe we can ask them to only use one set of parameters, and raise error if conflicts?

min_rel_progress=0.01,
enable_global_explain=False,
xgboost_version='0.9',
category_encoding_method='LABEL_ENCODING',
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Do they want to use label encoding on features? This is actually wrong. https://stackoverflow.com/a/34346937

I talked with jiashangliu@, but BQML won't change the default.

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