[SC-13352] Small fixes to TrainingTestDegradation test

validmind/tests/model_validation/sklearn/TrainingTestDegradation.py

@@ -30,16 +30,16 @@ def TrainingTestDegradation(
 
     The `TrainingTestDegradation` class serves as a test to verify that the degradation in performance between the
     training and test datasets does not exceed a predefined threshold. This test measures the model's ability to
-    generalize from its training data to unseen test data, assessing key classification metrics such as accuracy,
-    precision, recall, and f1 score to verify the model's robustness and reliability.
+    generalize from its training data to unseen test data, assessing key classification metrics such as precision,
+    recall, and f1 score to verify the model's robustness and reliability.
 
     ### Test Mechanism
 
-    The code applies several predefined metrics, including accuracy, precision, recall, and f1 scores, to the model's
+    The code applies several predefined metrics, including precision, recall, and f1 scores, to the model's
     predictions for both the training and test datasets. It calculates the degradation as the difference between the
     training score and test score divided by the training score. The test is considered successful if the degradation
-    for each metric is less than the preset maximum threshold of 10%. The results are summarized in a table showing
-    each metric's train score, test score, degradation percentage, and pass/fail status.
+    for each metric is less than the preset maximum threshold (default: 0.10). The results are summarized in a table
+    showing each metric's train score, test score, degradation percentage, and pass/fail status.
 
     ### Signs of High Risk