# Optimize Statistical Results

The topics in this section present useful tips and tricks to increase statistical performance of your VisionPro Deep Learning application.

## Optimize Precision, Recall, F-Score

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

| Blue Read Tool | Blue Locate Tool | Green Classify Tool | Red Analyze Tool
--- | --- | --- | --- | ---
Where to adjust | Feature location Feature score Match data | Feature location Feature score Match data | Match scores | Defect area Score
What's being adjusted | Feature Score | Feature Score | Class Score | Defect Probability

In addition, you can adjust each tool's Threshold parameter (which is one of the ).

Changing the Threshold parameter affects Recall and Precision in the following ways:

Threshold | Recall | Precision
--- | --- | ---
Lowering the Threshold | Increases | Decreases
Increasing the Threshold | Decreases | Increases

Changing the Threshold parameter affects False Positives and False Negatives in the following ways:

Threshold | False Positives | False Negatives
--- | --- | ---
Lowering the Threshold | Decreases | Increases
Increasing the Threshold | Increases | Decreases

If seeking a balanced rate of False Positive (FP) and False Negative (FN) results, seek to 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.

## Optimize 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.
  - Is usually much lower than the view-based AUC.

- Threshold setting depends on the requirements of the application.

  - High threshold to avoid false positives.
  - Low threshold to avoid false negatives.
  - Otherwise, a good compromise is %FP = %FN.