# Blue Locate Tool – Statistics

Table | Description
--- | ---
| Feature – The item that has been found. In this example, there were 10 classes of features found.
Found – Tells how many of the features were found.
Train – How many of the found objects were trained.
Recall – How likely the tool is to find the desired object. Recall is the ability of a the neural network to be able to find the desired feature where it actually exists. It is denoted as the number of correctly marked features divided by the number of labeled features. A high Recall score hopefully means that all labeled features are found. However, since recall does not take into account those that are incorrectly found, a high recall rate can also mean this.
Precision – How likely the tool is to find a feature that has been labeled. Precision is the neural network’s ability to correctly place a feature. It is denoted as the number of correctly guessed features over the total number of found features. Hopefully high precision means that all features found exist in the image. But high precision does not take into account all of the features that exist that were not found, so it can also mean the above, where we have many more features that have gone undetected.
F-Score – The weighted average of Precision and Recall.

Note:

- Precision and Recall vary when changing the Threshold parameter because the number of found features is not necessarily constant.

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

- If seeking a balanced rate of False Positive (FP) and False Negative (FN) results, seek to optimize the F-Score (both overall and averaged).
- A low Precision score is less problematic if you have a Model to filter out false positives.