In machine learning, what is a common method to assess a model's predictive performance?

Using confusion matrix metrics is a valuable method to assess a model's predictive performance because it provides a comprehensive view of how well the model is performing, especially in classification problems.

The confusion matrix summarizes the performance of a classification algorithm by showing the true positives, true negatives, false positives, and false negatives. From this matrix, metrics such as precision, recall, F1-score, and overall accuracy can be derived. These metrics allow for a nuanced evaluation of how the model is behaving beyond just a single accuracy score, which can be misleading, particularly in cases of class imbalance or when the costs of different types of errors vary significantly.

Therefore, utilizing confusion matrix metrics helps practitioners understand specific strengths and weaknesses of the model, enabling more informed decisions about model improvements or deployment.