# machinelearning-samples **Repository Path**: mirrors_dotnet/machinelearning-samples ## Basic Information - **Project Name**: machinelearning-samples - **Description**: Samples for ML.NET, an open source and cross-platform machine learning framework for .NET. - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 2 - **Created**: 2020-08-08 - **Last Updated**: 2025-09-27 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README > Note: We'd love to hear your thoughts about MLOps. Let us know in [this survey](https://www.research.net/r/mlops-samples). # ML.NET Samples [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) is a cross-platform open-source machine learning framework that makes machine learning accessible to .NET developers. In this GitHub repo, we provide samples which will help you get started with ML.NET and how to infuse ML into existing and new .NET apps. **Note:** Please open issues related to [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) framework in the [Machine Learning repository](https://github.com/dotnet/machinelearning/issues). Please create the issue in this repo only if you face issues with the samples in this repository. There are two types of samples/apps in the repo: * Getting Started ![](./images/app-type-getting-started-term-cursor.png) : ML.NET code focused samples for each ML task or area, usually implemented as simple console apps. * End-End apps ![](./images/app-type-e2e-black.png) : End-user sample web and desktop apps infused with Machine Learning models based on ML.NET. The official ML.NET samples are divided in multiple categories depending on the scenario and machine learning problem/task, accessible through the following tables:

Binary classification
Sentiment Analysis C# F#	Spam Detection C# F#	Credit Card Fraud Detection (Binary Classification) C# F#
Heart Disease Prediction C#
Multi-class classification
Issues Classification C# F#	Iris Flowers Classification C# F#	MNIST C#
Recommendation
Product Recommendation C#	Movie Recommender (Matrix Factorization) C#	Movie Recommender (Field Aware Factorization Machines) C#
Regression
Price Prediction C# F#	Sales Forecasting (Regression) C#	Demand Prediction C# F#
Time Series Forecasting
Sales Forecasting (Time Series) C#
Anomaly Detection
Sales Spike Detection C# C#	Power Anomaly Detection C#	Credit Card Fraud Detection (Anomaly Detection) C#
Clustering
Customer Segmentation C# F#	IRIS Flowers Clustering C# F#
Ranking
Rank Search Engine Results C#
Computer Vision
Image Classification Training (High-Level API) C# F#	Image Classification Predictions (Pretrained TensorFlow model scoring) C# F# C#	Image Classification Training (TensorFlow Featurizer Estimator) C# F#
Object Detection (ONNX model scoring) C# C#

Cross Cutting Scenarios

Scalable Model on WebAPI
C#

Scalable Model on Razor web app
C#

Scalable Model on Azure Functions
C#

Scalable Model on Blazor web app
C#

Large Datasets
C#

Loading data with DatabaseLoader
C#

Loading data with LoadFromEnumerable
C#

Model Explainability
C#

Export to ONNX
C#

# Automate ML.NET models generation (Preview state) The previous samples show you how to use the ML.NET API 1.0 (GA since May 2019). However, we're also working on simplifying ML.NET usage with additional technologies that automate the creation of the model for you so you don't need to write the code by yourself to train a model, you simply need to provide your datasets. The "best" model and the code for running it will be generated for you. These additional technologies for automating model generation are in PREVIEW state and currently only support *Binary-Classification, Multiclass Classification and Regression*. In upcoming versions we'll be supporting additional ML Tasks such as *Recommendations, Anomaly Detection, Clustering, etc.*. ## CLI samples: (Preview state) The ML.NET CLI (command-line interface) is a tool you can run on any command-prompt (Windows, Mac or Linux) for generating good quality ML.NET models based on training datasets you provide. In addition, it also generates sample C# code to run/score that model plus the C# code that was used to create/train it so you can research what algorithm and settings it is using. | CLI (Command Line Interface) samples | |----------------------------------| | [Binary Classification sample](/samples/CLI/BinaryClassification_CLI) | | [MultiClass Classification sample](/samples/CLI/MulticlassClassification_CLI) | | [Regression sample](/samples/CLI/Regression_CLI) | ## AutoML API samples: (Preview state) **THESE SAMPLES USE THE 0.1.x VERSION OF THE AUTOML API. WHILE THESE APIS STILL WORK IN VERSION 0.2.x WE RECOMMEND USING THE NEW APIS INTRODUCED IN 0.2.x AND LATER. FOR 0.2.x SAMPLES, SEE [ML.NET 2.0 Samples](samples/csharp/getting-started/MLNET2/README.md)**. ML.NET AutoML API is basically a set of libraries packaged as a NuGet package you can use from your .NET code. AutoML eliminates the task of selecting different algorithms, hyperparameters. AutoML will intelligently generate many combinations of algorithms and hyperparameters and will find high quality models for you. | AutoML API samples | |----------------------------------| | [Binary Classification sample](/samples/csharp/getting-started/BinaryClassification_AutoML) | | [MultiClass Classification sample](/samples/csharp/getting-started/MulticlassClassification_AutoML) | | [Ranking sample](/samples/csharp/getting-started/Ranking_AutoML/Ranking) | | [Regression sample](/samples/csharp/getting-started/Regression_AutoML) | | [Advanced experiment sample](/samples/csharp/getting-started/AdvancedExperiment_AutoML) | ------------------------------------------------------- # Additional ML.NET Community Samples In addition to the ML.NET samples provided by Microsoft, we're also highlighting samples created by the community showcased in this separated page: [ML.NET Community Samples](https://github.com/dotnet/machinelearning-samples/blob/main/docs/COMMUNITY-SAMPLES.md) Those Community Samples are not maintained by Microsoft but by their owners. If you have created any cool ML.NET sample, please, add its info into this [REQUEST issue](https://github.com/dotnet/machinelearning-samples/issues/86) and we'll publish its information in the mentioned page, eventually. ## Translations of Samples: - [Chinese Simplified](https://github.com/feiyun0112/machinelearning-samples.zh-cn) # Learn more See [ML.NET Guide](https://docs.microsoft.com/en-us/dotnet/machine-learning/) for detailed information on tutorials, ML basics, etc. # API reference Check out the [ML.NET API Reference](https://docs.microsoft.com/dotnet/api/?view=ml-dotnet) to see the breadth of APIs available. # Contributing We welcome contributions! Please review our [contribution guide](CONTRIBUTING.md). # Community Please join our community on Gitter [![Join the chat at https://gitter.im/dotnet/mlnet](https://badges.gitter.im/Join%20Chat.svg)](https://gitter.im/dotnet/mlnet?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) This project has adopted the code of conduct defined by the [Contributor Covenant](http://contributor-covenant.org/) to clarify expected behavior in our community. For more information, see the [.NET Foundation Code of Conduct](https://dotnetfoundation.org/code-of-conduct). # License [ML.NET Samples](https://github.com/dotnet/machinelearning-samples) are licensed under the [MIT license](LICENSE).

Cross Cutting Scenarios
Scalable Model on WebAPI C#	Scalable Model on Razor web app C#	Scalable Model on Azure Functions C#
Scalable Model on Blazor web app C#	Large Datasets C#	Loading data with DatabaseLoader C#
Loading data with LoadFromEnumerable C#	Model Explainability C#	Export to ONNX C#