100 Facts About Model Evaluation Metrics

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Here’s a list of 100 facts about model evaluation metrics:

1. Model evaluation metrics assess the performance of machine learning models.
2. Classification metrics are used for models predicting categorical outcomes, while regression metrics are used for models predicting continuous outcomes.
3. Common classification metrics include accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve.
4. Accuracy is the ratio of correctly predicted instances to the total number of instances.
5. Precision measures the accuracy of positive predictions.
6. Recall (Sensitivity) measures the ability of a model to identify all relevant instances.
7. F1 score is the harmonic mean of precision and recall, providing a balance between the two.
8. The ROC curve visualizes the trade-off between sensitivity and specificity at different classification thresholds.
9. Area under the ROC curve (AUC-ROC) quantifies the overall performance of a classification model.
10. Precision-Recall curves provide insights into the trade-off between precision and recall.
11. Area under the Precision-Recall curve (AUC-PR) is particularly informative for…