# False Positive and False Negative Results

In addition to the Statistical Results components, it is also important to understand how they affect False Positive and False Negative results.

Assume that there is an image inspection system that is built for capturing defects in images. If it captures one or more defects in an image, let's say the inspection result for that image is Positive, and if it captures no defect at all, the inspection result for that image is Negative. Then, the statistical results of the inspection task can be summarized into the followings:

- False Positive (also known as a Type I Error)

  - Red Analyze: An inspection system identifies a view or a pixel defective, but this view or pixel does not actually have a defect.

  - Green Classify: An inspection system identifies a class of an image, but this image does not actually belong to that class.

  - Blue Locate/Read: An inspection system identifies a class of a feature, but this feature does not actually belong to that class.

- False Negative (also known as a Type II Error)

  - Red Analyze: An inspection system could not identify a view or a pixel defective, but this view or pixel should have been identified defective.

  - Green Classify: An inspection system could not identify a class of an image, but this image should have been identified as belonging to that class.

  - Blue Locate/Read: An inspection system could not identify a class of a feature, but this feature should have been identified as belonging to that class.

### False Positives/Negatives and Precision/Recall

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. See Statistical Results to get the definitions of Precision and Recall.

- Precision
  - A neural network with a low Precision typically fails to correctly grab the defects 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 defects 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
  - A neural network with a low Recall typically fails to sufficiently grab the defects 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 defects 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)

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