diff --git a/07_regression.ipynb b/07_regression.ipynb
index 092f4f37d4c2347a34ab6d9dcf35eb6dbb834fc1..89119f0a27f42807abcc83a5eebadd474902a6d0 100644
--- a/07_regression.ipynb
+++ b/07_regression.ipynb
@@ -910,7 +910,10 @@
     "\n",
     "- `sklearn.tree.DecisionTreeRegressor` expands the concept of decision trees to regression [is documented here](https://scikit-learn.org/stable/modules/tree.html#regression).\n",
     "\n",
-    "- `sklearn.linear_model.TweedieRegressor`, `sklearn.linear_model.PoissonRegressor`, `sklearn.linear_model.GammaRegressor` offer so-called *Generalized Linear Models* (**GLM**) which might be of interest if your target values are bounded or discrete or if the error distribution is skewed. This is usually the case when your target values are event-based discrete counts/frequencies, or continuous amounts, durations, costs/prices, or rates/probabilities.  The [scikit-learn GLM tutorial](https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression)  provides a formal insight as well as tips for choosing GLM with some use case examples. Beyond that the [wikipedia article about generalized linear models](https://en.wikipedia.org/wiki/Generalized_linear_model#Intuition) gives a nice intuition and [this discussion](https://stats.stackexchange.com/questions/190763/how-to-decide-which-glm-family-to-use) provides a guide which GLM should be used when."
+    "- `sklearn.linear_model.TweedieRegressor`, `sklearn.linear_model.PoissonRegressor`, `sklearn.linear_model.GammaRegressor` offer so-called *Generalized Linear Models* (**GLM**) which might be of interest if your target values are bounded or discrete or if the error distribution is skewed. \n",
+    "   - This is usually the case when your target values are event-based discrete counts/frequencies, or continuous amounts, durations, costs/prices, or rates/probabilities.  The [scikit-learn GLM tutorial](https://scikit-learn.org/stable/modules/linear_model.html#generalized-linear-regression)  provides a formal insight as well as tips for choosing GLM with some use case examples. \n",
+    "   - Beyond that the [wikipedia article about generalized linear models](https://en.wikipedia.org/wiki/Generalized_linear_model#Intuition) gives a nice intuition and [this discussion](https://stats.stackexchange.com/questions/190763/how-to-decide-which-glm-family-to-use) provides a guide which GLM should be used when.\n",
+    "   - For assessing and hyperparameter-optimization of such General Linear Models you should also use [suitable metrics from scikit-learn](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-poisson-gamma-and-tweedie-deviances)."
    ]
   },
   {