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This project is for understanding and quantifying the errors in a machine learning or data analytic pipeline. Two approaches are explored. The first is using freezing and unfreezing of pipeline components (using optimization techniques like grid-search, random-search, Bayesian Optimization, Genetic Algorithms etc.). The second is using a gradient…

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