What are common evaluation metrics in ML?

Common evaluation metrics in machine learning (ML) are quantitative measures used to assess the performance of ML models. These metrics vary depending on the type of machine learning task (e.g., classification, regression, clustering). Below, I’ve outlined some of the most common evaluation metrics for different types of ML tasks:

### For Classification Tasks
1. Accuracy: The proportion of correct predictions (both true positives and true negatives) among the total number of cases examined.
2. Precision (Positive Predictive Value): The ratio of true positive predictions to the total number of positive predictions made (i.e., the number of true positives divided by the sum of true and false positives).
3. Recall (Sensitivity or True Positive Rate): The ratio of true positive predictions to the total number of actual positives (i.e., the number of true positives divided by the sum of true positives and false negatives).
4. F1 Score: The harmonic mean of precision and recall, providing a balance between the two metrics.
5. AUC-ROC Curve (Area Under the Receiver Operating Characteristics Curve): A plot that shows the performance of a classification model at all classification thresholds, with AUC reflecting the degree of separability achieved by the model.

### For Regression Tasks
1. Mean Absolute Error (MAE): The average of the absolute differences between the predicted values and the actual values.
2. Mean Squared Error (MSE): The average of the