Can you name some metrics for classification tasks? Tell me about each of them.

Classification tasks often use the following metrics:

• Accuracy — the proportion of correctly classified objects out of the total. Suitable for balanced classes.

• Precision — the ratio of true positive results to the sum of true positives and false positives. Important when minimizing false alarms.

• Recall — the ratio of true positives to the sum of true positives and false negatives. Important when minimizing missed positive cases.

• F1-score — the harmonic mean of Precision and Recall, balancing between them.

• ROC-AUC — the area under the ROC curve, indicating the model's quality at different classification thresholds.

• Confusion Matrix — a table showing the distribution of predictions across classes, helping to understand the types of errors.

Each metric is suitable for different tasks and depends on class balance and business goals.