As an AI engineer, I consistently deal with the assessment of model accuracy. The measure of performance differs depending on the given problem. For instance, in evaluation metrics like precision, recall, F1 score, or Area Under ROC curve are often adopted for classification problems.
In a project wherein I helped develop a breast cancer detection model, the objective was to classify whether a tumor is malignant or benign. In such a high-stakes situation, model accuracy couldn't be compromised. Hence, we leveraged confusion matrix, precision, recall, and F1-score. We used tools and libraries like Python’s NumPy, Pandas and Scikit-Learn to calculate these metrics. We managed to achieve a considerably high F1-score, showcasing the model's robust performance and reliability.