XGBoost has established itself as one of the most important machine learning algorithms due to its versatility and impressive performance.
Having used XGBoost for previous projects, I am always open to making its implementation faster, better, and cheaper.
My curiosity was piqued when I came across AutoXGB (https://github.com/abhishekkrthakur/autoxgb), an automated tool for simplifying the training, evaluation, and deployment of XGBoost models.
Given my work in the financial services sector where fraud is a significant concern, it would be an excellent opportunity to use credit card fraud data to assess how AutoXGB fares against the standard XGBoost setup usually used.
Context
XGBoost has established itself as one of the most important machine learning algorithms due to its versatility and impressive performance. Having used XGBoost for previous projects, I am always open to making its implementation faster, better, and cheaper. My curiosity was piqued when I came across AutoXGB (https://github.com/abhishekkrthakur/autoxgb), an automated tool for simplifying the training, evaluation, and deployment of XGBoost models. Given my work in the financial services sector where fraud is a significant concern, it would be an excellent opportunity to use credit card fraud data to assess how AutoXGB fares against the standard XGBoost setup usually used.