Line edits2

Author: lootle1

learn-pr/azure/optimize-model-performance-roc-auc/includes/2-receiver-operator-characteristic-curve.md

@@ -2,7 +2,7 @@ Classification models must assign a sample to a category. For example, it must u
 
 We can improve classification models in many ways. For example, we can ensure our data are balanced, clean, and scaled. We can also alter our model architecture and use hyperparameters to squeeze as much performance as we possibly can out of our data and architecture. Eventually, we find no better way to improve performance on our test (or hold-out) set and declare our model ready.
 
-Model tuning to this point can be complex, but we can use a final simple step to further improve how well our model works. To understand this, though, we need to go back to basics.
+Model tuning to this point can be complex, but we can use a final step to further improve how well our model works. To understand this, though, we need to go back to basics.
 
 ## Probabilities and categories
 

learn-pr/azure/optimize-model-performance-roc-auc/includes/4-compare-optimize-curves.md

@@ -10,7 +10,7 @@ Usually there isn't a single threshold that gives both the best true positive ra
 
 ## Comparing models with AUC
 
-You can use ROC curves to compare models to each other, just like you can with cost functions. An ROC curve for a model shows how well it will work for a variety of decision thresholds. At the end of the day, what's most important in a model is how it will perform in the real world, where there's only one decision threshold. Why then would we want to compare models using thresholds we'll never use? There are two answers for this.
+You can use ROC curves to compare models to each other just like you can with cost functions. An ROC curve for a model shows how well it will work for a variety of decision thresholds. At the end of the day, what's most important in a model is how it will perform in the real world, where there's only one decision threshold. Why then would we want to compare models using thresholds we'll never use? There are two answers for this.
 
 Firstly, comparing ROC curves in particular ways is like performing a statistical test that tells us not just that one model did better on this particular test set, but whether it's likely to continue to perform better in the future. This is out of the scope of this learning material, but it's worth keeping in mind.