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This repo explores feature selection and explainability in machine learning models that predict iteration success in Agile projects. We reduce features by 90% without loss in accuracy and highlight key drivers using state-of-the-art explainability tools.
The experiments are described in detail in notebook.ipynb.
About
This repo explores feature selection and explainability in machine learning models that predict iteration success in Agile projects. We reduce features by 90% without loss in accuracy and highlight key drivers using state-of-the-art explainability tools.