updated release notes

README.md

@@ -27,7 +27,7 @@ pip install shapiq
 You can explain your model with `shapiq.explainer` and visualize Shapley interactions with `shapiq.plot`.
 If you are interested in the underlying game theoretic algorithms, then check out the `shapiq.approximator` and `shapiq.games` modules.
 
-### 📈 Compute any-order feature interactions
+### Compute any-order feature interactions
 
 Explain your models with Shapley interactions:
 
@@ -104,7 +104,7 @@ interaction_values.plot_force(feature_names=...)
   <img width="800px" src="https://raw.githubusercontent.com/mmschlk/shapiq/main/docs/source/_static/images/motivation_sv_and_si.png" alt="An example Force Plot for the California Housing Dataset with Shapley Interactions">
 </p>
 
-### 📊 Visualize feature interactions
+### Visualize feature interactions
 
 A handy way of visualizing interaction scores up to order 2 are network plots.
 You can see an example of such a plot below.
@@ -126,6 +126,26 @@ The pseudo-code above can produce the following plot (here also an image is adde
   <img width="500px" src="https://raw.githubusercontent.com/mmschlk/shapiq/main/docs/source/_static/network_example2.png" alt="network_plot_example">
 </p>
 
+### Explain models not supported by SHAP
+
+With ``shapiq`` you can also explain models that are not supported by SHAP.
+For example, you can explain [TabPFN](https://github.com/PriorLabs/TabPFN) by making use of the _remove-and-recontextualize_ explanation paradigm implemented in ``shapiq.TabPFNExplainer``.
+
+```python
+import tabpfn, shapiq
+X, y = ...  # load your dataata
+model = tabpfn.TabPFNClassifier()
+model.fit(X, y)
+explainer = shapiq.TabPFNExplainer(model=model, data=X, labels=y, index="FSII")
+faithful_shapley_values = explainer.explain(X[0])
+faithful_shapley_values.plot_force()
+```
+
+<p align="center">
+  <img width="700px" src="https://raw.githubusercontent.com/mmschlk/shapiq/main/docs/source/_static/images/fsii_tabpfn_force_plot_example.png" alt="Force Plot of FSII values as derived from the example tabpfn notebook">
+</p>
+
+
 ## 📖 Documentation with tutorials
 The documentation of ``shapiq`` can be found at https://shapiq.readthedocs.io.
 If you are new to Shapley values or Shapley interactions, we recommend starting with the [introduction](https://shapiq.readthedocs.io/en/latest/introduction/) and the [basic tutorials](https://shapiq.readthedocs.io/en/latest/notebooks/basics.html).