Apart from the mentioned metrics, I often use the Matthews correlation coefficient (MCC) for binary classification tasks. It takes into account true positives, true negatives, false positives, and false negatives, providing a balanced measure that is particularly useful when dealing with imbalanced datasets. Another useful metric is the R-squared or coefficient of determination, which indicates the proportion of the variance in the target variable that is predictable from the input features in regression tasks. Overall, the choice of metric depends on the specific problem and the business goals associated with the machine learning model.

In addition to the metrics mentioned above, another important metric is the log loss or cross-entropy loss, which is frequently used for multi-class classification problems. It measures the dissimilarity between the predicted probabilities and true labels. Another commonly used metric for ranking problems is the mean average precision (MAP), which evaluates the precision at different recall levels. For anomaly detection tasks, metrics like precision at K and mean average precision at K are used to assess the models' performance at detecting anomalies within the top K predictions.

One commonly used metric is accuracy, which measures the percentage of correctly predicted labels. However, accuracy can be misleading when the classes are imbalanced. Other commonly used metrics include precision, recall, and F1-score, which are useful in scenarios where the cost of false positives or false negatives is different. Additionally, metrics like mean squared error (MSE) and mean absolute error (MAE) are used for regression tasks, while area under the receiver operating characteristic curve (AUC-ROC) is used for binary classification tasks.