You need to evaluate a classification model. Which metric can you use? ​​

There are several metrics you can use to evaluate a classification model. Here are a few:

1. Accuracy: This is the most intuitive performance measure. It is simply the ratio of correctly predicted observations to the total observations.

2. Precision: Precision looks at the ratio of correctly predicted positive observations to the total predicted positives. High precision relates to the low false positive rate.

3. Recall (Sensitivity): Recall is the ratio of correctly predicted positive observations to the all observations in actual class.

4. F1 Score: The F1 Score is the weighted average of Precision and Recall. It tries to find the balance between precision and recall.

5. Area Under Curve (AUC-ROC): AUC - ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes.

6. Log Loss: Log loss is the most important classification metric based on probabilities. It's hard to interpret raw log-loss values, but log-loss is still a good metric for comparing models. For any given problem, a lower log-loss value means better predictions.

7. Confusion Matrix: The confusion matrix is a handy presentation of the accuracy of a model with two or more classes. The table presents predictions on the x-axis and accuracy outcomes on the y-axis. The cells of the table are the number of predictions made by a machine learning algorithm.

Remember, the choice of metric depends on your business objective.