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Non-actionable models

Identifying failed experiments and models that have not helped with driving the desired business outcomes is important: it helps ensure resources are allocated to the areas with the most business impact. Below are three results from a churn prevention test based on the ML model output. Which one performed the best and should be chosen for the implementation in the main production systems?

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Machine Learning for Business

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