Metric	Description	Use Case
Accuracy	The proportion of correct predictions to total predictions.	Classification tasks
Precision	The proportion of true positive predictions in all positive predictions.	When false positives are costly.
Recall	The proportion of true positive predictions in all actual positives.	When false negatives are costly.
F1 Score	The harmonic mean of precision and recall.	When seeking a balance between precision and recall.
ROC-AUC Score	Area Under the Receiver Operating Characteristic curve.	Evaluating performance at various threshold settings.
Mean Squared Error (MSE)	The average of the squares of the errors between actual and predicted values.	Regression tasks
Mean Absolute Error (MAE)	The average of the absolute differences between actual and predicted values.	Regression tasks, when outliers are present and should not heavily influence the model.

Conclusion:

Selecting the right evaluation metric is pivotal in machine learning as it directly influences how the performance of a model is interpreted and whether the model is deemed suitable for deployment. The choice of metric depends on the specific requirements of the task, such as the importance of false positives vs. false negatives, the need for probabilistic outcomes, and the nature of the prediction task (classification vs. regression). Understanding the nuances of each metric allows for a more informed model evaluation process, leading to better decision-making and more effective machine learning solutions.