Minor visualization improvement in performance safety aspects

docs/source/evaluation-aspects/performance.rst

@@ -27,6 +27,7 @@ Currently, the following metrics are implemented:
 * **Matthews correlation coefficient (MCC)** is a metric used to evaluate the performance where different aspects are gathered within a single score. The metric ranges from -1 to +1, where a score of +1 indicates perfect predictions with no mistakes. A score of 0 suggests that the classifier's predictions are no better than random guessing, while a score of -1 indicates total disagreement between predictions and actual labels, with all predictions being incorrect.
 
 In the context of binary classification, we also report the following metrics:
+
 * **Specificity** complements sensitivity by measuring the classifier's ability to avoid false alarms in identifying negative instances. It quantifies the proportion of true negative instances that are correctly identified out of all actual negative instances. High specificity minimizes false positive errors, enhancing the classifier's ability to accurately identify negative instances.
 * **Negative predictive value** assesses the reliability of negative predictions made by the classifier. It quantifies the proportion of true negative predictions out of all negative predictions made by the classifier. Negative predictive value is particularly relevant in scenarios where correctly identifying negative instances is crucial.
 * **Informedness** measures the classifier's ability to make correct positive and negative predictions simultaneously. Informedness ranges from -1 to 1, where a score of 1 indicates perfect classification, 0 indicates random classification, and -1 indicates perfectly incorrect classification.