Evaluation metrics are essential for assessing the performance of classification algorithms. They provide valuable insights into how well the model is performing on the given dataset. Here are some commonly used evaluation metrics for classification algorithms:

1. Accuracy: Accuracy measures the proportion of correctly classified instances out of the total instances in the dataset. It is a simple and widely used metric, but it may not be suitable for imbalanced datasets.

2. Precision: Precision is the ratio of true positive predictions to the total predicted positive instances. It represents the accuracy of positive predictions made by the model.

3. Recall (Sensitivity or True Positive Rate): Recall is the ratio of true positive predictions to the total actual positive instances. It measures the model's ability to correctly identify positive instances.

4. F1 Score: The F1 score is the harmonic mean of precision and recall. It provides a balance between precision and recall, especially for imbalanced datasets.

5. Specificity (True Negative Rate): Specificity is the ratio of true negative predictions to the total actual negative instances. It measures the model's ability to correctly identify negative instances.

6. Area Under the Receiver Operating Characteristic Curve (AUC-ROC): The AUC-ROC represents the area under the ROC curve, which is a plot of true positive rate (sensitivity) against the false positive rate (1 - specificity). It provides an aggregate measure of the model's performance across different classification thresholds.

7. Area Under the Precision-Recall Curve (AUC-PR): The AUC-PR represents the area under the precision-recall curve, which plots precision against recall. It is useful for imbalanced datasets, as it focuses on the positive class's performance.

8. Matthews Correlation Coefficient (MCC): MCC takes into account true positive, true negative, false positive, and false negative predictions. It is suitable for imbalanced datasets and yields a value between -1 and +1, where +1 indicates perfect predictions, 0 indicates random predictions, and -1 indicates complete disagreement between predictions and actual labels.

9. Confusion Matrix: The confusion matrix provides a comprehensive breakdown of true positive, true negative, false positive, and false negative predictions, giving more detailed insights into the model's performance.

10. Cohen's Kappa: Cohen's Kappa measures the agreement between the predicted and actual labels, considering the agreement that could occur by chance.

The choice of evaluation metrics depends on the specific problem, the class distribution in the dataset, and the model's objectives. It is often recommended to consider multiple metrics to get a holistic view of the model's performance.

https://www.dataspoof.info/post/top-10-evaluation-metrics-for-classification-models/