Feature Engineering with GenAI for Classification
This workflow uses GenAI to engineer features for supervised machine learning. The tasks is a binary classification problem to decide whether a bank should approve or reject a loan request advanced by an applicant. For comparison, the workflow displays two machine learning pipelines:
ML pipeline to predict loan status
ML pipeline to predict loan status enriched with AI-engineered features
Both pipelines use the XGBoost Tree Ensemble and its performance is optimized by selecting relevant features and tuning hyper-parameters.
In the "Supervision" component, the performance of the model with and without AI-engineered features is compared, AI-engineered features can be accepted (and saved), or rejected and technical support is requested per email.