In a well-trained model, what are the expected typical score ranges focused on?

In a well-trained model, the expected typical score ranges usually indicate how effective the model is in achieving its intended task. Scores that suggest higher accuracy and reliability are generally more indicative of a well-performing model.

The typical score ranges often reflect percentages or scores where a model is effective, suggesting that a small variance (such as falling between 70 or 75) may indicate that the model is adequately capturing the important patterns in the data. Therefore, option C, which includes scores such as 20 or 25, would indicate a low performance level, making it an unlikely candidate for a well-trained model, which typically wouldn't score in such a low range for reliability and effectiveness evaluations.

On the other hand, score ranges that include 70 or 75 are closer to the general expectations for acceptable model performance, suggesting these scores would likely indicate a well-functioning and reliable model.

In summary, the focus on score ranges for assessing model training typically targets the higher ranges, where performance and reliability are expected, and thus, the other options fall short in providing ranges that would generally characterize a well-trained model.