Is there a description of the automl method? about lightautoml

[alexmryzhkov]

@PGijsbers sure - we are going to provide it here and in readme as soon as it becomes available.

[alexmryzhkov]

@PGijsbers now we have an answer for this issue - https://arxiv.org/abs/2109.01528 😎

[alexmryzhkov]

Hello, @PGijsbers!

The documentation can be found here (https://lightautoml.readthedocs.io/en/latest/). Right now it is only an API description and tutorials, but we are working on filling it with algorithm descriptions. We already have the written paper, and now we are waiting for a decision on it before publication.

For hyperparameter optimization, we use a hybrid TPE and CMA-ES sampler. As a part of our pipeline, 2 model types are used - linear model and gradient boosting (LightGBM, CatBoost). For gradient boosting, we use hyperparameters tuning as long as it fits in the user-defined time budget. Feature engineering is different for each model type, and the same strategy is used to solve specific ML tasks on each dataset - during this strategy execution LightAutoML assumes the best handling method to convert each feature using metadata and expert rules.

[PGijsbers]

Thanks! Would it be possible to post back here when the paper is available? I'm interested 👀

[PGijsbers]

Do you have any estimate? Maybe a preprint to look forward to?