Use mse instead of "brier"

Author: MarkDaoust

site/en/tutorials/structured_data/imbalanced_data.ipynb

@@ -1233,7 +1233,7 @@
       "source": [
         "Here you can see that with class weights the accuracy and precision are lower because there are more false positives, but conversely the recall and AUC are higher because the model also found more true positives. Despite having lower accuracy, this model has higher recall (and identifies more fraudulent transactions than the baseline model at threshold 50%). Of course, there is a cost to both types of error (you wouldn't want to bug users by flagging too many legitimate transactions as fraudulent, either). Carefully consider the trade-offs between these different types of errors for your application.\n",
         "\n",
-        "Compared to the baseline model with changed threshold, the class weighted model is clearly inferior. The superiority of the baseline model is further confirmed by the lower test loss value (cross entropy and Brier score) and additionally can be seen by plotting the ROC curves of both models together."
+        "Compared to the baseline model with changed threshold, the class weighted model is clearly inferior. The superiority of the baseline model is further confirmed by the lower test loss value (cross entropy and mean squared error) and additionally can be seen by plotting the ROC curves of both models together."
       ]
     },
     {