[P] Automatic hyperparameter tuning for CatBoost and LightGBM

Hey friends,

I have developed a library, called **100gecs**, that makes hyperparameter tuning on LightGBM and CatBoost models trivially easy.

**Background**

LightGBM and CatBoost are gradient boosted tree models, like XGBoost, and in many cases the best baseline model in supervised learning tasks on tabular data. They work by iteratively fitting trees on data, with each subsequent tree "correcting" on some level the prediction of the prior tree. [Here](https://neptune.ai/blog/when-to-choose-catboost-over-xgboost-or-lightgbm)'s a good intro, if you want some more background on these …

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