Merge pull request #7346 from MicrosoftDocs/repo_sync_working_branch

Confirm merge from repo_sync_working_branch to main to sync with https://github.com/MicrosoftDocs/azure-ai-docs (branch main)

Author: Taojunshen

articles/machine-learning/how-to-understand-automated-ml.md

@@ -303,7 +303,7 @@ The predictions with confidence scores greater than the score threshold are outp
 
 The metric computation of an image object detection and instance segmentation model is based on an overlap measurement defined by a metric called **IoU** ([Intersection over Union](https://en.wikipedia.org/wiki/Jaccard_index)) which is computed by dividing the area of overlap between the ground-truth and the predictions by the area of union of the ground-truth and the predictions. The IoU computed from every prediction is compared with an **overlap threshold** called an IoU threshold, which determines how much a prediction should overlap with a user-annotated ground-truth in order to be considered as a positive prediction. If the IoU computed from the prediction is less than the overlap threshold the prediction wouldn't be considered as a positive prediction for the associated class.
 
-The primary metric for the evaluation of image object detection and instance segmentation models is the **mean average precision (mAP)**. The mAP is the average value of the average precision(AP) across all the classes. Automated ML object detection models support the computation of mAP using the below two popular methods.
+The primary metric for the evaluation of image object detection and instance segmentation models is the **mean average precision (mAP)**. The mAP is the average value of the average precision (AP) across all the classes. Automated ML object detection models support the computation of mAP using the below two popular methods.
 
 **Pascal VOC metrics**: