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This talk approaches the typical data science workflow with a focus on explainability. Simply put, it focuses on skills and tactics used to help data scientists articulate their findings to end-users, stake-holders, and other data scientists. From data ingestion, cleaning and feature selection, and ultimately model selection, explainability can be incorporated into a data scientist's workflow.Using a combination of semi-automated and open source software, this talk walks you through an explainable workflow.Please my on demand webcast, Explainable Workflows using Python: DSE Presents Chat with the Lab here. Reply with any of your questions!
You can watch the full webinar and demo here. Please share your questions below and the slides can be downloaded here.
Here is the GitHub you are looking for! My apologies for the delay!https://github.com/decentdilettante/XAI