Model evaluation metrics in machine learning are used to assess the performance and effectiveness of a trained model. The choice of evaluation metrics depends on the specific task and the nature of the problem being solved. Here are some commonly used model evaluation metrics in machine learning:

• Accuracy : Measures the proportion of correct predictions out of the total number of predictions. It is commonly used for balanced datasets and provides a general measure of the model's correctness.

• Precision : Measures the proportion of true positive predictions out of all positive predictions. It is useful when the focus is on minimizing false positives.

• Recall (Sensitivity or True Positive Rate): Measures the proportion of true positive predictions out of all actual positive instances. It is useful when the goal is to minimize false negatives.

• F1 Score: The harmonic mean of precision and recall, providing a single metric that balances both measures. It is especially useful when there is an imbalance between the classes.

• Specificity (True Negative Rate): Measures the proportion of true negative predictions out of all actual negative instances. It is useful when the goal is to minimize false positives.

• Area Under the ROC Curve (AUC-ROC): Summarizes the overall performance of the model by plotting the true positive rate against the false positive rate at different classification thresholds. Higher AUC values indicate better discrimination between classes.

• Mean Absolute Error (MAE): Measures the average absolute difference between predicted and actual values. It is useful for regression tasks where the magnitude of the errors is important.

• Mean Squared Error (MSE): Measures the average squared difference between predicted and actual values. It is commonly used in regression tasks, giving higher weight to larger errors compared to MAE.

• Root Mean Squared Error (RMSE): The square root of MSE, providing a measure of the average magnitude of the errors in the same units as the target variable. It is commonly used in regression tasks.

• R-squared (Coefficient of Determination): Measures the proportion of the variance in the target variable that can be explained by the model. It ranges from 0 to 1, with higher values indicating better model fit.

These are just a few of the many evaluation metrics used in machine learning. The choice of metrics should be based on the specific problem, the nature of the data, and the desired outcomes. It is often recommended to consider multiple metrics to get a comprehensive understanding of the model's performance.

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