Feature Engineering with GenAI for Classification

<p><strong>Feature Engineering with GenAI for Classification</strong></p><p>This workflow uses GenAI to engineer features for <strong>supervised machine learning</strong>. The tasks is a binary classification problem to decide whether a bank should <strong>approve or reject a loan request</strong> advanced by an applicant. For comparison, the workflow displays two machine learning pipelines:</p><ol><li><p>ML pipeline to predict loan status</p></li><li><p>ML pipeline to predict loan status enriched with AI-engineered features</p></li></ol><p>Both pipelines use the <strong>XGBoost Tree Ensemble </strong>and its performance is optimized by selecting relevant features and tuning hyper-parameters.</p><p>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.</p>