What indicates the level of confidence in the predictions made by a machine learning model?

The confidence score associated with each prediction is a crucial metric that reflects how certain the machine learning model is about its predictions. This score is typically a value between 0 and 1, where a score closer to 1 indicates higher confidence in the prediction, while a score closer to 0 signifies lower confidence. For instance, in classification tasks, a model might provide a confidence score indicating the likelihood that a given input belongs to a specific class, helping users understand how reliable the prediction is.

In contrast, while the accuracy percentage of the model can provide insights into overall model performance, it does not convey confidence for individual predictions. Computation complexity relates to the resources required to train or run the model but does not express confidence levels in predictions. The size of the training dataset can influence model performance but again does not directly indicate how much confidence the model has in particular predictions. Thus, the confidence score is the most direct and informative measure of a model's predictive certainty.