There are several common evaluation metrics used in machine learning to assess the performance of a model. The choice of evaluation metric depends on the type of problem and the specific goals of the model. Here are some commonly used evaluation metrics:

  1. Accuracy: Accuracy measures the proportion of correct predictions out of the total number of predictions. It is suitable for balanced datasets where the target class is evenly distributed.

  2. Precision: Precision is the ratio of true positive predictions to the sum of true positive and false positive predictions. It measures the ability of the model to correctly identify positive predictions, and it is useful when the cost of false positives is high.

  3. Recall: Recall, also known as sensitivity or true positive rate, is the ratio of true positive predictions to the sum of true positive and false negative predictions. It measures the ability of the model to correctly identify positive instances, and it is useful when the cost of false negatives is high.

  4. F1 Score: The F1 score is the harmonic mean of precision and recall. It provides a single metric that balances both precision and recall. F1 score is commonly used when there is an imbalance between the two classes.

  5. Area Under the ROC Curve (AUC-ROC): ROC stands for Receiver Operating Characteristic, and the AUC-ROC is a measure of the classifier's ability to distinguish between classes. It plots the true positive rate against the false positive rate at various threshold settings. A perfect classifier will have an AUC-ROC score of 1.0.

  6. Mean Absolute Error (MAE): MAE calculates the average absolute difference between the predicted values and the true values. It is commonly used for regression problems where the focus is on the magnitude of errors.

  7. Mean Squared Error (MSE): MSE calculates the average of the squared differences between the predicted values and the true values. It is also commonly used for regression problems but penalizes larger errors more than MAE.

  8. R-Squared (R2): R-squared measures how well the model fits the data. It quantifies the proportion of variation in the target variable that can be explained by the independent variables. A value of 1.0 indicates a perfect fit, and a value of 0.0 indicates no relationship.

  These are some of the common evaluation metrics used in machine learning. It is important to select the appropriate metric based on the problem and goals to effectively evaluate and compare models.