Across
- 2. Measures the proportion of true positive predictions among all positive predictions made by the model.
- 6. Measures the proportion of actual negatives that are correctly identified as such by the model.
- 8. Instances correctly classified as positive by the model.
- 9. Instances incorrectly classified as negative when they are actually positive.
- 10. Instances correctly identified as negative by the model.
Down
- 1. Also known as recall, it measures the proportion of actual positives that are correctly identified by the model.
- 3. A prediction mistake where the model wrongly identifies a negative instance as positive.
- 4. The proportion of misclassified instances in the dataset.
- 5. The ratio of correctly predicted instances to the total instances in the dataset.
- 7. The harmonic mean of precision and recall, balancing the two metrics.