Update index.html

index.html

@@ -13,35 +13,48 @@
           <p>
             This is a demo of MLTest on the dataset
             <a href="https://huggingface.co/datasets/marmal88/skin_cancer"><code>marmal88/skin_cancer</code></a>.
+            Each image is labeled with one of three different kinds of cancers.
           </p>
           <p>
             The model has been trained on five models: two variants of Swin Transformers, ViT, ResNet, and BEiT. The test results for each model can
             be inspected in the dashboard below.
           </p>
           <p>
-            <b>Performance tests</b>: TODO
+            <b>Performance tests</b>:
+            in order to measure how well the model performs, we compute common performance metrics like accuracy,
+            precision, recall, F1 score, and more.
           </p>
           <p>
-            <b>Failure clusters</b>: TODO
+            <b>Failure clusters</b>:
+            these clusters give meaningful insights when the model is failing and can be inspected in the "Failure Clusters" tab.
+            These failure clusters are automatically detected
+            for different combinations of metadata.
+            For example, the BEiT transformer has a significantly lower accuracy on images taken of cancers of the back with class label <code>0</code>.
           </p>
           <p>
-            <b>Robustness</b>: TODO
-            The following robustness tests were enabled:
-                  - kind: Brightness
-      - kind: CompressImage
-      - kind: Contrast
-      - kind: DarkSpots
-      - kind: GaussianBlur
-      - kind: GaussianNoise
-      - kind: Glare
-      - kind: GlassBlur
-      - kind: HorizontalFlip
-      - kind: MedianBlur
-      - kind: MotionBlur
-      - kind: OilSpots
-      - kind: Perspective
-      - kind: VerticalFlip
+            <b>Robustness</b>: these tests help ML developers evaluate how well their model performs under different conditions.
+            These conditions could include different levels of brightness, compression, and many other types of interference.
+          </p><p>
+            The following robustness tests were enabled for this test case:
+          </p>
+          <ul>
+            <li>kind: Brightness</li>
+            <li>kind: CompressImage</li>
+            <li>kind: Contrast</li>
+            <li>kind: DarkSpots</li>
+            <li>kind: GaussianBlur</li>
+            <li>kind: GaussianNoise</li>
+            <li>kind: Glare</li>
+            <li>kind: GlassBlur</li>
+            <li>kind: HorizontalFlip</li>
+            <li>kind: MedianBlur</li>
+            <li>kind: MotionBlur</li>
+            <li>kind: OilSpots</li>
+            <li>kind: Perspective</li>
+            <li>kind: VerticalFlip</li>
+          </ul>
 
+          <p>
             The full list of transforms supported by MLTest can be found in the <a href="https://docs.lakera.ai/configuration/robustness">documentation</a>.
           </p>
           <p>