How is the threshold selected and what criterion is used?

Threshold in machine learning tasks is usually selected based on the analysis of model quality metrics such as accuracy, recall, F1-score, ROC-AUC, and others, depending on the task.

Process of threshold selection:

1. Define the goal: For example, in a classification task, it is important to balance false positives and false negatives.
2. Analyze ROC curve or Precision-Recall curve: These graphs show how metrics change with the threshold.
3. Choose a criterion: You can select a threshold that maximizes the F1-score (balance of precision and recall) or minimizes a specific type of error.
4. Validation: Check the chosen threshold on a hold-out set or through cross-validation.

For example, if it is more important not to miss positive cases (maximize recall), the threshold can be lowered to increase the model's sensitivity.

Thus, the criterion for threshold selection depends on the business task and the trade-off between different types of errors.