mlnet

name	mlnet
description	Use when building custom machine learning models in .NET with ML.NET. Covers data loading, training pipelines, prediction engines, AutoML, model evaluation, and deployment for classification, regression, clustering, and anomaly detection. USE FOR: training custom ML models in .NET, binary and multi-class classification, regression and forecasting, clustering and anomaly detection, AutoML experimentation, model serialization and deployment DO NOT USE FOR: running pre-trained ONNX models (use onnx), calling cloud-hosted LLMs (use microsoft-extensions-ai or azure-ai-inference), building AI agent workflows (use agent-framework), deep learning with GPU training (use TorchSharp or Python)
license	MIT
metadata	{"displayName":"ML.NET","author":"Tyler-R-Kendrick","version":"1.0.0"}
compatibility	claude, copilot, cursor
references	[{"title":"ML.NET Documentation","url":"https://learn.microsoft.com/dotnet/machine-learning"},{"title":"ML.NET GitHub Repository","url":"https://github.com/dotnet/machinelearning"},{"title":"Microsoft.ML NuGet Package","url":"https://www.nuget.org/packages/Microsoft.ML"}]

ML.NET

Overview

ML.NET is Microsoft's cross-platform machine learning framework for .NET developers. It provides a pipeline-based API for loading data, transforming features, training models, and making predictions without requiring deep ML expertise. ML.NET supports classification, regression, clustering, anomaly detection, recommendation, and time-series forecasting, with AutoML for automated model selection and hyperparameter tuning.

NuGet Packages

dotnet add package Microsoft.ML
dotnet add package Microsoft.ML.AutoML           # AutoML experimentation
dotnet add package Microsoft.ML.TimeSeries       # Time-series forecasting
dotnet add package Microsoft.ML.Recommender      # Recommendation engine
dotnet add package Microsoft.ML.ImageAnalytics   # Image classification

Data Classes

ML.NET uses POCOs (Plain Old C# Objects) to represent input data and predictions.

using Microsoft.ML.Data;

public class HouseData
{
    [LoadColumn(0)] public float Size { get; set; }
    [LoadColumn(1)] public float Bedrooms { get; set; }
    [LoadColumn(2)] public float Bathrooms { get; set; }
    [LoadColumn(3)] public float Age { get; set; }
    [LoadColumn(4)] public float Price { get; set; }
}

public class HousePrediction
{
    [ColumnName()]
      PredictedPrice { ; ; }
}

  
{
    []  ? Text { ; ; }
    []   Sentiment { ; ; }
}

  
{
    []
      Prediction { ; ; }
      Probability { ; ; }
      Score { ; ; }
}