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LICENSE		LICENSE
README.md		README.md
main.py		main.py
titanic.csv		titanic.csv
train_ebm.py		train_ebm.py

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xai-interpretml

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This project applies Microsoft's InterpretML library to analyze Titanic survival data using two ML models: the Explainable Boosting Machine (EBM), a fully transparent glassbox model, and Random Forest, a blackbox model. The EBM allows each prediction to be traced back to specific features such as gender, age, and passenger class.

bias-detection ebm explainable-ai interpretml random-forest

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