AI-Engineered Feature Creation

Data Cleaning, Normalization and Feature Selection

Model Training & Testing

Data Cleaning, Normalization and Feature Selection

1: Regular Machine Learning Pipeline

2: Machine Learning Pipeline with AI-Engineered Features

Model Training & Testing

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:

1. ML pipeline to predict loan status

2. ML pipeline to predict loan status enriched with AI-engineered features

Both pipelines use the XGBoost Tree Ensamble 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-generated features is compared, AI-generated features can be accepted (and saved), or rejected and technical support is requested per email.

Data access