# Blue Locate Tool – Statistics

In the Database Overview of Blue Locate, there are 2 tables and each shows different results. The one above displays the result metrics for each feature in the images, and the other one below displays the result metrics for each model in the images. Here, the model stands for one of the Node and Layout Model, not a neural network model of the Blue Locate.

A "labeled model" is a model that is created (labeled) by users on views. In the processing step, Blue Locate uses it to tell whether this model is found on a view or not. So, a "found model" is a model that is found on views by a trained Blue Locate.

A found model is said to be a correct match (correctly marked model) if there is a labeled model in approximately the same position and if this found model has features that are close to the corresponding labeled features. The precision, recall, and F-score of model are calculated on this ground, and if there are multiple models in a view all model metrics are calculated by each model separately.

Note: Also refer to the Blue Read Tool – Statistics topics, because both the Blue Locate and Blue Read use the same statistical methods to measure the tool's results.

The Metrics for Features

Table | Metrics | Description
--- | --- | ---
| Feature | The name of the feature.
Found | Tells how many feature objects of this feature class were found as the result of processing in all views.
Train | Tells how many feature objects of this feature class were included (labeled) in the training set.
Labeled | Tells how many feature objects of this feature class were included (labeled) in all views.
Recall | How likely the tool is to find the labeled feature objects. Recall is the ability of a 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, even with a high recall rate there still can be Type I errors. See False Positive and False Negative Results for the details.
Precision | How precisely 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 marked 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 there still can be Type II errors, where we have many more features that have gone undetected. See False Positive and False Negative Results for the details.
F-Score | The harmonic mean of Precision and Recall.

The Metrics for Models

Table | Metrics | Description
--- | --- | ---
| Model | The name of the model.
Found | Tells how many models were found as the result of processing in all views.
Train | Tells how many labeled models were included in the training set.
Labeled | Tells how many labeled models were there in all views.
Recall | How likely the tool is to find the labeled models. Recall is the ability of a neural network to be able to find the desired model where it actually exists. It is denoted as the number of correctly marked models divided by the number of labeled models. A high Recall score hopefully means that all labeled models are found. However, since recall does not take into account those that are incorrectly found, even with a high recall rate there still can be Type I errors. See False Positive and False Negative Results for the details.
Precision | How precisely the tool is to find a model that has been labeled. Precision is the neural network’s ability to correctly place a model. It is denoted as the number of correctly marked models over the total number of found models. Hopefully high precision means that all models found exist in the image. But high precision does not take into account many models that exist and but were not found, so there still can be Type II errors, where we have many more models that have gone undetected. See False Positive and False Negative Results for the details.
F-Score | The harmonic mean of Precision and Recall.

Note:

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

  Threshold | 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.