# Evaluating Results from a Blue Tool

In the Database Overview of a Blue tool, 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 model, Layout model, or another kind of model used with the tool, not the neural network model of the tool.

A "labeled model" is a model that is created (labeled) by users on views. In the processing step, the Blue tool 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 tool.

A found model is 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: 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.
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.
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.
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.
F-Score | The harmonic mean of Precision and Recall.