# Precision, Recall, F-Score

The False Positives and False Negatives are then summarized and represented again using the following metrics, which are the Precision and Recall, the statistical results used in all VisionPro Deep Learning tools.

- Precision is the percentage of the detected character features that match the labeled character features.
  - A neural network with a low Precision typically fails to correctly grab the features that should have been detected from given image data (test data), and so it returns many False Positive judgments (Type 1 Errors).
  - A neural network with a high Precision typically succeeds to correctly grab the features from given image data (test data), but if combined with low Recall, there is a possibility yet that there could be many False Negative judgments (Type 2 Errors)
- Recall is the percentage of detected character features that are correctly identified by the tool.
  - A neural network with a low Recall typically fails to sufficiently grab the features that should have been detected from given image data (test data), and so it returns many False Negative judgments (Type 2 Errors).
  - A neural network with a high Recall typically succeeds to sufficiently grab the features from given image data (test data), but if combined with low Precision, there is a possibility yet that there could be many False Positive judgments (Type 1 Errors)
- F-Score is the harmonic mean of Recall and Precision.

The ideal statistical results for almost all inspection cases (there could be exceptional cases) includes high Precision and high Recall at the same time.

Note: For how to optimize the precision, recall, and F1 Score, see .