diff --git a/.github/workflows/explainerdashboard.yml b/.github/workflows/explainerdashboard.yml
index cff7d0c..c56c899 100644
--- a/.github/workflows/explainerdashboard.yml
+++ b/.github/workflows/explainerdashboard.yml
@@ -41,4 +41,4 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        pytest
+        pytest -k "not selenium"
diff --git a/README.md b/README.md
index de074ab..0f8ade6 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@ that explains the workings of a (scikit-learn compatible) machine
 learning model. The dashboard provides interactive plots on model performance, 
 feature importances, feature contributions to individual predictions, 
 "what if" analysis,
-partial dependence plots, SHAP (interaction) values, visualisation of individual
+partial dependence plots, SHAP (interaction) values, visualization of individual
 decision trees, etc. 
 
 You can also interactively explore components of the dashboard in a 
@@ -218,23 +218,25 @@ There are a few tricks to make this less painful:
     values can be very slow to calculate, and often are not needed for analysis.
     For permutation importances you can set the `n_jobs` parameter to speed up
     the calculation in parallel.
-2. Storing the explainer. The calculated properties are only calculated once
+2. Calculate approximate shap values. You can pass approximate=True as a shap parameter by
+   passing `shap_kwargs=dict(approximate=True)` to the explainer initialization. 
+4. Storing the explainer. The calculated properties are only calculated once
     for each instance, however each time when you instantiate a new explainer
     instance they will have to be recalculated. You can store them with
     `explainer.dump("explainer.joblib")` and load with e.g. 
     `ClassifierExplainer.from_file("explainer.joblib")`. All calculated properties
     are stored along with the explainer.
-3. Using a smaller (test) dataset, or using smaller decision trees. 
+5. Using a smaller (test) dataset, or using smaller decision trees. 
     TreeShap computational complexity is `O(TLD^2)`, where `T` is the 
     number of trees, `L` is the maximum number of leaves in any tree and 
     `D` the maximal depth of any tree. So reducing the number of leaves or average
     depth in the decision tree can really speed up SHAP calculations.
-4. Pre-computing shap values. Perhaps you already have calculated the shap values
+6. Pre-computing shap values. Perhaps you already have calculated the shap values
     somewhere, or you can calculate them off on a giant cluster somewhere, or
     your model supports [GPU generated shap values](https://github.com/rapidsai/gputreeshap). 
     You can simply add these pre-calculated shap values to the explainer 
     with `explainer.set_shap_values()` and `explainer.set_shap_interaction_values()` methods.
-5. Plotting only a random sample of points. When you have a lots of observations,
+7. Plotting only a random sample of points. When you have a lots of observations,
     simply rendering the plots may get slow as well. You can pass the `plot_sample`
     parameter to render a (different each time) random sample of observations
     for the various scatter plots in the dashboard. E.g.: 
@@ -536,7 +538,7 @@ In order to reduce the memory footprint there are a number of things you can do:
 2. Setting a lower precision. By default shap values are stored as `'float64'`,
     but you can store them as `'float32'` instead and save half the space:
     ```ClassifierExplainer(model, X_test, y_test, precision='float32')```. You 
-    can also set a lower precision on your `X_test` dataset yourself ofcourse.
+    can also set a lower precision on your `X_test` dataset yourself of course.
 3. For multi class classifier, by default `ClassifierExplainer` calculates
     shap values for all classes. If you're only interested in a single class
     you can drop the other shap values: `explainer.keep_shap_pos_label_only(pos_label)`