# Optimizing Statistical Results

Each of the VisionPro Deep Learning tools have a tolerance that can be adjusted, which allows the tool to be selective in its predictions.

Tool | Where to Adjust | What Is Adjusted
Blue Read | Feature location Feature score Match data | Feature Score
Blue Locate | Feature location Feature score Match data | Feature Score
Green Classify | Match scores | Class Score
Red Analyze | Defect area Score | Defect Probability

In addition, you can adjust the Threshold parameter of each tool as follows:

- Lowering the Threshold parameter has the following effects:

  - Increases Recall.

  - Decreases Precision.

  - Decreases the number of False Positives.

  - Increases the number of False Negatives.

- Increasing the Threshold parameter has the following effects:

  - Decreases Recall.

  - Increases Precision.

  - Increases the number of False Positives.

  - Decreases the number of False Negatives.

If seeking a balanced rate of False Positive and False Negative results, optimize the F-Score (both overall and averaged).

Note: For Blue Locate, a low precision score is less problematic if you have a Model to filter out false positives.

## Optimizing Red Analyze Results

To optimize the performance of the Red Analyze tool, keep in mind the following:

- Prefer the AUC of untrained items.

- Prefer region-based AUC. This requires that you mark all bad samples. This is usually much lower than the view A view of an image is a region of pixels in an image. Tool processing is limited to the pixels within the view. You can manually specify a view, or you can use the results of an upstream tool to generate a view.-based AUC.

- Threshold setting depends on the requirements of the application. Use high threshold to avoid false positives or a low threshold to avoid false negatives. Otherwise, a good compromise is %FP = %FN.