Part II: Manual Feature Engineering techniques for the Kaggle Home Credit Default Competition
Blog post from Comet
The Comet.ml team continues their participation in the Kaggle Home Credit Default Competition, focusing on creating a comprehensive dataset for their models by incorporating features from seven different datasets, with insights from mortgage professionals. This involves manual feature engineering, assisted by subject matter experts, to identify key features indicative of an applicant's default risk. The manual feature engineering led to a slight improvement in model performance, with an AUC score increase from 0.745 to 0.7519. The team connected with industry professionals Cody Dadiw and Philippa Stewart-Donnelly, who provided valuable insights into feature selection while highlighting the importance of human oversight in machine learning applications within the financial sector. This collaboration underscores the potential benefits of combining human expertise with machine learning for better decision-making, emphasizing the need for ethical considerations and compliance with financial regulations. The blog also reflects on the limitations of data-driven models in capturing the nuanced human aspects of lending, advocating for hybrid approaches that integrate human judgment with AI.
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