Update concepts-features.md

Author: jcodella

articles/cognitive-services/personalizer/concepts-features.md

@@ -332,7 +332,7 @@ JSON objects can include nested JSON objects and simple property/values. An arra
 Personalizer can help you to understand which features of a chosen action are the most and least influential to then model during inference. When enabled, inference explainability includes feature scores from the underlying model into the Rank API response, so your application receives this information at the time of inference.
 Feature scores empower you to better understand the relationship between features and the decisions made by Personalizer. They can be used to provide insight to your end-users into why a particular recommendation was made, or to further analyze how the data is being used by the underlying model.
 
-Setting the service configuration flag IsInferenceExplainabilityEnabled in your service configuration enables Personalizer to include feature values and weights in the Rank API response. To update your current service configuration, use the [Service Configuration – Update API](/rest/api/personalizer/1.1preview1/service-configuration/update?tabs=HTTP). In the JSON request body, include your current service configuration and add the additional entry: “IsInferenceExplainabilityEnabled”: true. If you don’t know your current service configuration, you can obtain it from the [Service Configuration – Get API](/rest/api/personalizer/1.1preview1/service-configuration/get?tabs=HTTP)
+Setting the service configuration flag IsInferenceExplainabilityEnabled in your service configuration enables Personalizer to include feature values and weights in the Rank API response. To update your current service configuration, use the [Service Configuration – Update API](/rest/api/personalizer/1.1preview1/service-configuration/update?tabs=HTTP). In the JSON request body, include your current service configuration and add the additional entry: `“IsInferenceExplainabilityEnabled”: true`. If you don’t know your current service configuration, you can obtain it from the [Service Configuration – Get API](/rest/api/personalizer/1.1preview1/service-configuration/get?tabs=HTTP)
 
 ```JSON
 {
@@ -396,7 +396,7 @@ Enabling inference explainability will add a collection to the JSON response fro
 }
 ```
 
-In the example above, three action IDs are returned in the _ranking_ collection along with their respective probabilities scores. The action with the largest probability is the_ best action_ as determined by the model trained on data sent to the Personalizer APIs, which in this case is `"id": "EntertainmentArticle"`. The action ID can be seen again in the _inferenceExplanation_ collection, along with the feature names and scores determined by the model for that action and the features and values sent to the Rank API. 
+In the example above, three action IDs are returned in the _ranking_ collection along with their respective probabilities scores. The action with the largest probability is the _best action_ as determined by the model trained on data sent to the Personalizer APIs, which in this case is `"id": "EntertainmentArticle"`. The action ID can be seen again in the _inferenceExplanation_ collection, along with the feature names and scores determined by the model for that action and the features and values sent to the Rank API. 
 
 Recall that Personalizer will either return the _best action_ or an _exploratory action_ chosen by the exploration policy. The best action is the one that the model has determined has the highest probability of maximizing the average reward, whereas exploratory actions are chosen among the set of all possible actions provided in the Rank API call. Actions taken during exploration do not leverage the feature scores in determining which action to take, therefore **feature scores for exploratory actions should not be used to gain an understanding of why the action was taken.** [You can learn more about exploration here](/azure/cognitive-services/personalizer/concepts-exploration).