DOC explain proper scoring rule in one sentence.

Author: lorentzenchr

site/en/tutorials/structured_data/imbalanced_data.ipynb

@@ -446,7 +446,7 @@
         "#### Metrics for probability predictions\n",
         "\n",
         "As we train our network with the cross entropy as a loss function, it is fully capable of predicting class probabilities, i.e. it is a probabilistic classifier.\n",
-        "Metrics that assess probabilistic predictions and that are, in fact, **proper scoring rules** are:\n",
+        "Good metrics to assess probabilistic predictions are, in fact, **proper scoring rules**. Their key property is that predicting the true probability is optimal. We give two well-known examples:\n",
         "\n",
         "*   **cross entropy** also known as log loss\n",
         "*   **Brier score** also known as squared error\n",
@@ -1308,8 +1308,18 @@
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