Some common measures of success for a 测试集 (test set) can include the following:

  1. Accuracy: Accuracy measures the percentage of correctly classified instances in the test set. It is a widely used measure of success for classification problems.

  2. Precision and Recall: Precision measures the proportion of true positive predictions out of all positive predictions, while recall measures the proportion of true positive predictions out of all actual positive instances. These measures are commonly used in binary classification tasks where false positives or false negatives have different consequences.

  3. F1 Score: The F1 score is a weighted average of precision and recall, providing a single measure that reflects both metrics. It is particularly useful when the dataset is imbalanced, and the goal is to have high precision and recall simultaneously.

  4. Mean Squared Error (MSE): MSE is commonly used as a measure of success for regression problems. It calculates the average squared difference between predicted and actual values. Lower MSE indicates better performance.

  5. Area Under the Curve (AUC): AUC is a measure of the performance of a binary classification model. It represents the probability of a randomly chosen positive instance ranking higher than a randomly chosen negative instance. A higher AUC indicates better model performance.

  6. R-Squared: R-Squared is a measure of how well a regression model fits the data. It represents the proportion of the variance in the dependent variable that is predictable from the independent variable. Higher R-squared values indicate a better fit.

  7. Mean Absolute Error (MAE): MAE measures the average absolute difference between predicted and actual values. It is commonly used as a metric for regression tasks and provides a straightforward interpretation of the average prediction error.

  It's important to note that the choice of measures depends on the specific problem and context. Different measures may be more appropriate for different scenarios.