Overfitting when using AutoML

Hi,

We used FLAML to perform regression task, and found AutoML model was easy to be overfitted. However, in the same task, other ML models e.g., LightGBM, RF, could avoid overfitting by grid search best parameters. We tried add 'cv=5' into the AutoML model, but it did not work on our case.

So could you give me some suggestions on how to avoid overfitting when using FLAML AutoML models?

BTW:
We also used `flame.default.LGBMRegressor()` to perform auto-search hyper-parameters of LightGBM model, but this model is still overfitting. But LightGBM model could be avoid overfitting by grid search methods. So I think maybe I misuse FLAML.

Lu Li

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The code of FLAML AutoML models:
`from flaml import AutoML`
`am = AutoML()`
`am.fit(x_train, y_train, task="regression")`

The performance on training data:

The performance on test data: