The LightGBM transformation library aims at providing a flexible and automatic way to do feature transformation when using LightGBM. Compared to separate transformation. this way has several pros:
- More efficient. Data preprocessing can go with parsing each line, and take advantage of multi-processing designed by lightgbm naturally. No need to store whole transformed data in file/memory.
- More convenient for development and iteration. Built-in transformation could keep offline and online consistent well by saved/loaded along with model.
In the repo, user could learn about:
- How to customize favorite parser by playing with LightGBM Parser interface.
- How to use
FreeForm2Parser, a powerful and efficient tool built in the repo, in model training. Instead of handling data yourself, user just need to prepare a feature spec which contains feature name, transform type and expression, and make slight changes in experiment iterations.
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