Fix wrongly documented model info result columns

Author: Mats-SX

doc/modules/ROOT/pages/machine-learning/linkprediction-pipelines/training.adoc

@@ -90,12 +90,12 @@ The `modelInfo` return field has the following algorithm-specific subfields:
 .Fields of modelInfo
 [opts="header",cols="1,1,6"]
 |===
-| Name                    | Type          | Description
-| modelName               | String        | The name of the trained model.
-| modelType               | String        | The type of the trained model.
-| bestParameters          | Map           | The model parameters which performed best on average on validation folds according to the primary metric.
-| metrics                 | Map           | Map from metric description to evaluated metrics for the winning model over the subsets of the data, see below.
-| trainingPipeline        | Map           | The pipeline used for the training.
+| Name             | Type   | Description
+| modelName        | String | The name of the trained model.
+| modelType        | String | The type of the trained model.
+| bestParameters   | Map    | The model parameters which performed best on average on validation folds according to the primary metric.
+| metrics          | Map    | Map from metric description to evaluated metrics for the winning model over the subsets of the data, see below.
+| pipeline         | Map    | Steps to produce input features for the pipeline model.
 |===
 
 
@@ -105,7 +105,7 @@ The structure of `modelInfo` is:
 ----
 {
     bestParameters: Map,        // <1>
-    trainingPipeline: Map       // <2>
+    pipeline: Map               // <2>
     metrics: {                  // <3>
         AUCPR: {
             test: Float,        // <4>

doc/modules/ROOT/pages/machine-learning/node-property-prediction/nodeclassification-pipelines/training.adoc

@@ -116,13 +116,13 @@ The `modelInfo` return field has the following algorithm-specific subfields:
 .Fields of modelInfo
 [opts="header",cols="1,1,6"]
 |===
-| Name                    | Type          | Description
-| modelName               | String        | The name of the trained model.
-| modelType               | String        | The type of the trained model.
-| classes                 | List of Integer | Sorted list of class ids which are the distinct values of `targetProperty` over the entire graph.
-| bestParameters          | Map           | The model parameters which performed best on average on validation folds according to the primary metric.
-| metrics                 | Map           | Map from metric description to evaluated metrics for the winning model over the subsets of the data, see below.
-| trainingPipeline        | Map           | The pipeline used for the training.
+| Name            | Type            | Description
+| modelName       | String          | The name of the trained model.
+| modelType       | String          | The type of the trained model.
+| classes         | List of Integer | Sorted list of class ids which are the distinct values of `targetProperty` over the entire graph.
+| bestParameters  | Map             | The model parameters which performed best on average on validation folds according to the primary metric.
+| metrics         | Map             | Map from metric description to evaluated metrics for the winning model over the subsets of the data, see below.
+| pipeline        | Map             | Steps to produce input features for the pipeline model.
 |===
 
 The structure of `modelInfo` is:
@@ -131,7 +131,7 @@ The structure of `modelInfo` is:
 ----
 {
     bestParameters: Map,        // <1>
-    trainingPipeline: Map       // <2>
+    pipeline: Map               // <2>
     classes: List of Integer,   // <3>
     metrics: {                  // <4>
         <METRIC_NAME>: {        // <5>

doc/modules/ROOT/pages/machine-learning/node-property-prediction/noderegression-pipelines/training.adoc

@@ -93,10 +93,10 @@ The `modelInfo` return field has the following algorithm-specific subfields:
 .Model info fields
 [opts="header",cols="1,1,6"]
 |===
-| Name                    | Type            | Description
-| bestParameters          | Map             | The model parameters which performed best on average on validation folds according to the primary metric.
-| metrics                 | Map             | Map from metric description to evaluated metrics for the winning model over the subsets of the data, see below.
-| pipeline                | Map             | The pipeline used to generate and select the node features.
+| Name            | Type  | Description
+| bestParameters  | Map   | The model parameters which performed best on average on validation folds according to the primary metric.
+| metrics         | Map   | Map from metric description to evaluated metrics for the winning model over the subsets of the data, see below.
+| pipeline        | Map   | Steps to produce input features for the pipeline model.
 |===
 
 The structure of `modelInfo` is:
@@ -348,4 +348,3 @@ RETURN
 As we can see, the results indicate a lower mean square error for the random forest model, compared to `nr-pipeline-model` in earlier section.
 The change is due to the embeddings taking into account more contextual information.
 While this is a toy example, additional context can sometimes provide valuable information to pipeline steps, resulting in better performance.
-