Determining when to stop active ML runs

Active ML runs are dynamic and iterative processes that require careful monitoring, as we have already seen. But they also require strategic decision-making to determine the optimal point for cessation. The decision to stop an active ML run is critical as it impacts both the performance and efficiency of the learning model. This section focuses on the key considerations and strategies to effectively determine when to stop active machine learning runs.

In active ML, establishing clear performance goals specific to the project is crucial. For instance, consider a project aimed at developing a facial recognition system. Here, accuracy and precision might be the chosen performance metrics. A diverse test set, mirroring real-world conditions and varied facial features, is crucial for evaluating the model.

Let’s say the pre-defined threshold on the established test set for accuracy is set at 95% and for precision, at 90%. The active ML...