👉 Criterion computing is a framework for evaluating and comparing the performance of machine learning models based on a single, well-defined criterion or metric. This approach simplifies the evaluation process by focusing on one key aspect of model performance, such as accuracy, precision, recall, or F1 score, rather than requiring a multi-faceted assessment. By using a single criterion, criterion computing facilitates a more straightforward and interpretable comparison between different models, especially in scenarios where multiple metrics might conflict or provide conflicting insights. This method is particularly useful in applications where a clear, overarching goal or objective is essential for model selection and optimization, such as in medical diagnosis, autonomous driving, or financial forecasting.
