|  | Element | Description |
|---|---|---|
| ![](images/icon-1.png) | Filter by predicted or actual |  Narrows the display based on the predicted and actual class values. See [Filters](#filters) for details.|
| ![](images/icon-2.png) | Show color overlay | Sets whether to display the activation map in either black and white or full color. See [Color overlay](#color-overlay) for details. |
| ![](images/icon-3.png) | Activation scale | Shows the extent to which a region is influencing the prediction. See [Activation scale](#activation-scale) for details. |

See the [reference material](vai-ref#ref-map) for detailed information about Visual AI.

### Filters {: #filters }

Filters allow you to narrow the display based on the predicted and the actual class values. The initial display shows the full sample (i.e., both filters are set to *all*). You can instead set the display to filter by specific classes, limiting the display). Some examples:

| "Predicted" filter |  "Actual" filter  |   Display results  |
|--------------------|-------------------|--------------------|
| All    | All  | All (up to 100) samples from the validation set |
| Tomato Leaf Mold  | All  | All samples in which the predicted class was Tomato Leaf Mold                                   |
| Tomato Leaf Mold  | Tomato Leaf Mold | All samples in which both the predicted and actual class were Tomato Leaf Mold   |
| Tomato Leaf Mold  | Potato Blight  | Any sample in which DataRobot predicted Tomato Leaf Mold but the actual class was potato blight |


Hover over an image to see the reported predicted and actual classes for the image:

![](images/vai-20.png)


### Color overlay {: #color-overlay }

DataRobot provides two different views of the activation maps&mdash;black and white (which shows some transparency of original image colors) and full color. Select the option that provides the clearest contrast. For example, for black and white datasets, the alternative color overlay may make activation areas more obvious (instead of using a black-to-transparent scale). Toggle **Show color overlay** to compare.

![](images/vai-18.png)


### Activation scale {: #activation-scale }

The high-to-low activation scale indicates how much of a region in an image is influencing the prediction. Areas that are higher on the scale have a higher predictive influence&mdash;the model used something that was there (or not there, but should have been) to make the prediction. Some examples might include the presence or absence of yellow discoloration on a leaf, a shadow under a leaf, or an edge of a leaf that curls in a certain way.

Another way to think of scale is that it reflects how much the model "is excited by" a particular region of the image. It’s a kind of prediction explanation&mdash;why did the model predict what it did? The map shows that the reason is because the algorithm saw _x_ in this region, which activated the filters sensitive to visual information like _x_.