ml-engineer-pro video for as an ML engineer at a manufacturing company, you're currently working on a predictive maintenance project. The goal is to create a
As an ML engineer at a manufacturing company, you're currently working on a predictive maintenance project. The goal is to create a classification model that predicts whether a critical machine will experience a failure within the next three days. This predictive capability allows the repair team to address potential issues before they lead to a breakdown. While routine maintenance for the machine is cost-effective, a failure can result in significant expenses. You've trained multiple binary classifiers to make predictions about the machine's failure, where a prediction of 1 indicates the model foresees a failure. Now, during the evaluation phase on a separate dataset, you face the decision of selecting a model that emphasizes detection. However, you also need to ensure that over 50% of the maintenance tasks initiated by your model are genuinely related to impending machine failures. Which model should you opt for to achieve this balance?