# Confusion Matrix for Green Classify

The Confusion Matrix is a visual representation of the ground truth versus the tool's predictions. The Confusion Matrix of the Green Classify tool is a graphical representation of the Recall and Precision scores. 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 View Browser.

| 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

The color of a confusion matrix cell stands for the ratio of the number of predicted views (the value of a cell) to the total number of labeled views (the row sum). The darker color means the higher ratio and the lighter means the opposite.

For example, the color of the cell at the index (1,1) is dark red because among the views whose label is 'Bauble' the number of views classified as 'Bauble' is 18 and it accounts for 75% (18/24) of the views whose label is 'Bauble.'

For another example, the color of the cell at the index (1,2) is yellow because among the views whose label is 'Bauble' the number of views classified as 'Beef' is 2. The number of views classified as 'Beef' accounts for 8.3% (2/24) of the views whose label is 'Bauble.'

It is generally recommended that the darker cells are placed at the diagonal indices of a confusion matrix because it represents the desirable results.

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.