我注意到 H2O 已经发布了目标均值编码
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-munging/target-encoding.html http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-munging/target-encoding.html
它仅附带一个 R 代码示例。有人有Python 的例子吗？
像这样：
from h2o.targetencoder import TargetEncoder
# Fit target encoding on training data
targetEncoder = TargetEncoder(x= ["addr_state", "purpose"], y = "bad_loan", fold_column = "cv_fold_te")
targetEncoder.fit(ext_train)
但这需要版本至少为3.22
这是示例的链接：https://github.com/h2oai/h2o-tutorials/blob/78c3766741e8cbbbd8db04d54b1e34f678b85310/best-practices/feature-engineering/feature_engineering.ipynb https://github.com/h2oai/h2o-tutorials/blob/78c3766741e8cbbbd8db04d54b1e34f678b85310/best-practices/feature-engineering/feature_engineering.ipynb
以及代码本身的链接：https://github.com/h2oai/h2o-3/blob/master/h2o-py/h2o/targetencoder.py https://github.com/h2oai/h2o-3/blob/master/h2o-py/h2o/targetencoder.py
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