diff --git a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
index 81017ba..a08093e 100644
--- a/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
+++ b/the_seventy_maxims_of_maximally_effective_machine_learning_engineers.myco
@@ -3,7 +3,7 @@ Based on [[https://schlockmercenary.fandom.com/wiki/The_Seventy_Maxims_of_Maxima
 *. Preprocess, then train.
 *. A training loop in motion outranks a perfect architecture that isn’t implemented.
 *. A debugger with a stack trace outranks everyone else.
-*. Regularization covers a multitude of overfitting sins.
+*. Regularization covers a multitude of sins.
 *. Feature importance and data leakage should be easier to tell apart.
 *. If increasing model complexity wasn’t your last resort, you failed to add enough layers.
 *. If the accuracy is high enough, stakeholders will stop complaining about the compute costs.