few notes

Author: alelavml3

md-docs/user_guide/model.md

@@ -1,8 +1,9 @@
 # Model
 
-In the ML Cube Platform, a Model is a representation of the actual machine learning model used for making predictions. The data used
+In the ML Cube Platform, a Model is a representation of the actual artificial intelligence model used for making predictions. The data used
 for its training usually represent the reference data distribution, while production data comprises the data on which the model 
 performs inference.
+For more information about reference and production data see the [Data] page.
 
 A Model is uniquely associated with a [Task] and it can be created both through the WebApp and the Python SDK. 
 Currently, we support only one model per Task.
@@ -39,13 +40,20 @@ the ML cube Platform will use this information to compute additional metrics and
 It is optional and currently supported only for Classification and RAG tasks. If specified, the probabilistic output must be provided 
 as a new column in the predictions file, following the guidelines in the [Data Schema] page.
 
+!!! example
+    For example, Logistic Regression classification model provides both the probability of belonging to the positive class and the predicted class using a threshold.
+    In this case, you can upload to ML cube Platform the predicted class as principal prediction and the probability as probabilistic output.
+
 ### Model Metric
 
 A Model Metric represents the evaluation metric used to assess the performance of the model. 
 It can both represent a performance or an error. The chosen metric will be used in the various views of the WebApp to
 provide insights on the model's performance and in the [Performance View](modules/retraining.md#performance-view) section
 of the Retraining Module.
 
+!!! note
+    Note that model metrics can only be computed when target data are available.
+
 The available options are:
 
 | Metric            | Task Type                  |
@@ -81,4 +89,5 @@ from the ML cube Platform. More information on how to set up a retrain trigger c
 
 [Task]: task.md
 [Data Schema]: data_schema.md#subrole
-[Retrain Trigger]: integrations/retrain_trigger.md
+[Retrain Trigger]: integrations/retrain_trigger.md
+[Data]: data.md