Skill ML.NET Regression

Predict a continuous value from multiple input tags using ML.NET FastTree Regression. The AI generates the full C# Script Class with multi-feature training pipeline, connects it to live tags, creates output tags, and configures model persistence.

When to Use This Skill

User chose Regression — FastTree in the ML.NET router skill (Step 0)
Predicting a continuous value from 2–5 input features
User goals: energy consumption prediction, process output modeling, production rate from multiple inputs

Prerequisites

Solution open with input feature tags and label tag already created and receiving data
Solution target platform set to Multiplatform (ML.NET requires .NET 8+)
The user has confirmed: (1) which tags are features, (2) which tag is the label (value to predict), (3) what the predicted value represents

MCP Tools and Tables

Category	Items
Tools	`get_table_schema`, `write_objects`, `get_objects`
Tables	`UnsTags`, `ScriptsClasses`, `ScriptsTasks`

Step 1: Create Output Tags

get_table_schema('UnsTags')

{
  "table_type": "UnsTags",
  "data": [
    { "Name": "<AssetPath>/ML/PredictedValue", "DataType": "Double", "Description": "Model predicted value" },
    { "Name": "<AssetPath>/ML/LastPrediction", "DataType": "DateTime", "Description": "Timestamp of last prediction" }
  ]
}

Replace <AssetPath> with the actual asset folder path (e.g., Plant/Reactor1).

Step 2: Create the Script Class

ML.NET Namespace Declaration

Critical: The field AddMLNetNamespaces does not exist and is silently ignored. Always use NamespaceDeclarations.

"NamespaceDeclarations": "Microsoft.ML;Microsoft.ML.Data;Microsoft.ML.Transforms;Microsoft.ML.Transforms.TimeSeries;Microsoft.ML.Transforms.Text;Microsoft.ML.Trainers;Microsoft.ML.TimeSeries"

Tag References Inside Script Classes

Always use @Tag. prefix. ML.NET expects float but FrameworX tags use double — always cast with (float) when feeding ML.NET and (double) when writing to tags.

FastTree Regression Pipeline

var pipeline = mlContext.Transforms.Concatenate("Features", "Feature1", "Feature2", "Feature3")
    .Append(mlContext.Regression.Trainers.FastTree(
        labelColumnName: "Label",
        featureColumnName: "Features",
        numberOfLeaves: 20,
        numberOfTrees: 100,
        minimumExampleCountPerLeaf: 10,
        learningRate: 0.2));

Output: float Score (predicted continuous value)

Full Class Example — Regression

public class ProcessData
{
    public float Feature1 { get; set; }  // e.g., Temperature
    public float Feature2 { get; set; }  // e.g., Pressure
    public float Feature3 { get; set; }  // e.g., Flow
    public float Label { get; set; }     // e.g., EnergyConsumption (what we predict)
}

public class RegressionPrediction
{
    public float Score { get; set; }
}

private static MLContext mlContext = new MLContext(seed: 0);
private static ITransformer model;
private static IDataView lastTrainingDataView;
private static PredictionEngine<ProcessData, RegressionPrediction> predictionEngine;
private static bool modelTrained = false;
private static List<ProcessData> trainingBuffer = new List<ProcessData>();
private const int MinTrainingSize = 200;
private static readonly string ModelPath = Path.Combine(@Info.GetExecutionPath(), "<ClassName>.mlnet");

public void Predict(double input1, double input2, double input3, double label)
{
    trainingBuffer.Add(new ProcessData
    {
        Feature1 = (float)input1,
        Feature2 = (float)input2,
        Feature3 = (float)input3,
        Label = (float)label
    });

    if (!modelTrained && trainingBuffer.Count >= MinTrainingSize)
        TrainModel();

    if (modelTrained)
        RunPrediction(input1, input2, input3);
}

public void LoadModel()
{
    if (File.Exists(ModelPath))
    {
        model = mlContext.Model.Load(ModelPath, out _);
        predictionEngine = mlContext.Model.CreatePredictionEngine<ProcessData, RegressionPrediction>(model);
        modelTrained = true;
    }
}

private void TrainModel()
{
    lastTrainingDataView = mlContext.Data.LoadFromEnumerable(trainingBuffer);
    var pipeline = mlContext.Transforms.Concatenate("Features",
            nameof(ProcessData.Feature1),
            nameof(ProcessData.Feature2),
            nameof(ProcessData.Feature3))
        .Append(mlContext.Regression.Trainers.FastTree(
            labelColumnName: "Label",
            featureColumnName: "Features",
            numberOfLeaves: 20,
            numberOfTrees: 100,
            minimumExampleCountPerLeaf: 10,
            learningRate: 0.2));

    model = pipeline.Fit(lastTrainingDataView);
    predictionEngine = mlContext.Model.CreatePredictionEngine<ProcessData, RegressionPrediction>(model);
    modelTrained = true;
    SaveModel();
}

private void SaveModel()
{
    mlContext.Model.Save(model, lastTrainingDataView.Schema, ModelPath);
}

private void RunPrediction(double input1, double input2, double input3)
{
    var input = new ProcessData
    {
        Feature1 = (float)input1,
        Feature2 = (float)input2,
        Feature3 = (float)input3
    };
    var result = predictionEngine.Predict(input);

    @Tag.<AssetPath>/ML/PredictedValue.Value = (double)result.Score;
    @Tag.<AssetPath>/ML/LastPrediction.Value = DateTime.Now;
}

Note on training data: The Predict() method accepts a label parameter during training. This is the known actual value the model learns to predict. After training, the label is not needed for prediction-only calls. The AI should adapt the method signature based on whether the user has a label tag or trains from historical data.

Step 3: Write the Class via MCP

get_table_schema('ScriptsClasses')

{
  "table_type": "ScriptsClasses",
  "data": [
    {
      "Name": "<ClassName>",
      "Code": "CSharp",
      "Domain": "Server",
      "ClassContent": "Methods",
      "NamespaceDeclarations": "Microsoft.ML;Microsoft.ML.Data;Microsoft.ML.Transforms;Microsoft.ML.Transforms.TimeSeries;Microsoft.ML.Transforms.Text;Microsoft.ML.Trainers;Microsoft.ML.TimeSeries",
      "Contents": "<AI-generated C# code from full class example above>"
    }
  ]
}

Field names matter: Code = language (not Language), Contents = code body (not Code). Wrong names = silent data loss.

Step 4: Create the Trigger

Task (Periodic) — best for multi-input models

get_table_schema('ScriptsTasks')

{
  "table_type": "ScriptsTasks",
  "data": [
    {
      "Name": "ML_Predict_Periodic",
      "Language": "CSharp",
      "Execution": "Periodic",
      "Period": 5000,
      "Code": "@Script.Class.<ClassName>.Predict(\n    @Tag.Plant/Reactor1/Temperature.Value,\n    @Tag.Plant/Reactor1/Pressure.Value,\n    @Tag.Plant/Reactor1/Flow.Value,\n    @Tag.Plant/Reactor1/EnergyConsumption.Value);"
    }
  ]
}

The @ prefix is mandatory when referencing runtime objects inside ScriptsTasks. Without it: CS0234.

ServerStartup — always wire LoadModel

Read the existing ServerStartup task first (document object — read-modify-write), then append:

Script.Class.<ClassName>.LoadModel();

Step 5: Verify

Confirm Multiplatform — ML.NET requires .NET 8+. Instruct the user: “Solution → Settings → Target Platform = Multiplatform, then Product → Modify.”
Do NOT start the runtime automatically.
Wait for training — needs MinTrainingSize data points (default 200 for regression)
Check output tags — verify LastPrediction updates and PredictedValue is reasonable

Common Pitfalls

Mistake	Why It Happens	How to Avoid
Missing ML.NET namespaces	Used `AddMLNetNamespaces: true`	Always set `NamespaceDeclarations`
`CS0234` in ScriptsTasks	Missing `@` prefix	Always `@Script.Class.Name.Predict()`
`CS0246` Class not found	Build order issue	Set `BuildOrder: "1"` on ML Script Class
`System.Math` load error	.NET 4.8 target	Switch to Multiplatform
Wrong data types	ML.NET=float, tags=double	Cast `(float)`/`(double)`
Model lost on restart	SaveModel/LoadModel missing	Include both + ServerStartup
PredictionEngine null	Forgot to create after loading	Call `CreatePredictionEngine` in LoadModel
Feature count mismatch	Concatenate names don't match data class	Use `nameof()` for feature names in Concatenate

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