# Green Classify Tool – Confusion Matrix

The Confusion Matrix of the Green Classify tool is a graphical representation of the Recall and Precision scores.

Good Performance | Poor Performance

Variables in Confusion Matrix Table

Variable | Description
Tag | The class label.
Found | The total number of the views classified (predicted, marked) as this class.
Train | The total number of the views labeled as this class and included in the training set.
Labeled | The total number of the views labeled as this class.
n/c (Not classified) | The proportion of the views that are not in the training set and whose scores (class probabilities) are under the threshold.
Recall | The percentage of the correctly classified views among the views that are labeled as this class and that are not in the training set. See the section below for the details.
Precision | The percentage of the correctly classified views among the views that are classified as this class and that are not in the training set. See the section below for the details.
F-Score | The harmonic mean of the precision and recall calculated above.

Precision, Recall, F-Score Calculation Example

The precision and recall are calculated with the data that is not used for training the tool. F-Score is the harmonic mean of the precision and the recall and that being so it is also based on the data that are not in the training set. You can manually figure out the same values as the ones in the Confusion Matrix table using Display Filter.

- Recall calculation example for the class "Beef" (Display Filter Syntax)

  - True Positives: best_tag='Beef' and tag![name='Beef'] and not trained

  - False Negatives: best_tag!='Beef' and tag![name='Beef'] and not trained

  - Recall = True Positives / (True Positives + False Negatives)

- Precision calculation example for the class "Beef" (Display Filter Syntax)

  - True Positives: best_tag='Beef' and tag![name='Beef'] and not trained

  - False Positives: best_tag='Beef' and tag![name!='Beef'] and not trained

  - Precision = True Positives / (True Positives + False Positives)

- F-Score calculation example for the class "Beef"

Confusion Matrix Details

The confusion matrix on the left side is the representation of the right table. If you click each circle at the Confusion Matrix on Database Overview, the corresponding item (views) in each cell of the table will be displayed on the VisionPro Deep Learning Tool GUI.

|  | Predicted Class
Bauble | Beef | Grizzly | Key | Nuts
Actual Class | Bauble | 18 | 2 | 2 | 2 |
Beef |  | 23 |  |  | 1
Grizzly |  |  | 24 |  |
Key | 6 |  |  | 18 |
Nuts |  | 2 |  |  | 22