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ML.NET 示例
ML.NET 是一个跨平台的开源机器学习框架，使.NET开发人员使用机器学习变得很容易。
在这个GitHub 存储库中，我们提供了示例，这些示例将帮助您开始使用ML.NET，以及如何将ML.NET加入到现有的和新的.NET应用程序中。
注意: 请在机器学习存储库中打开与ML.NET框架相关的问题。请仅当您遇到此存储库中的示例问题时，才在存储库中创建该问题。
存储库中有两种类型的示例/应用程序：
-
入门
: 针对每个机器学习任务或领域的ML.NET代码示例，通常作为简单的控制台应用程序实现。 -
终端应用程序
: 使用ML.NET进行机器学习的Web，桌面，移动和其他应用程序的实际例子
根据场景和机器学习问题/任务，官方ML.NET示例被分成多个类别，可通过下表访问：
<table align="middle" width=100%> <tr> <td align="middle" colspan="3">二元分类</td> </tr> <tr> <td align="middle"><img src="images/sentiment-analysis.png" alt="Binary classification chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>情绪分析<br><a href="samples/csharp/getting-started/BinaryClassification_SentimentAnalysis">C#</a> <a href="samples/fsharp/getting-started/BinaryClassification_SentimentAnalysis">F#</a></b></td> <td align="middle"><img src="images/spam-detection.png" alt="Movie Recommender chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>垃圾信息检测<br><a href="samples/csharp/getting-started/BinaryClassification_SpamDetection">C#</a> <a href="samples/fsharp/getting-started/BinaryClassification_SpamDetection">F#</a></b></td> <td align="middle"><img src="images/anomaly-detection.png" alt="Power Anomaly detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>信用卡欺诈识别<br>(Binary Classification)<br><a href="samples/csharp/getting-started/BinaryClassification_CreditCardFraudDetection">C#</a> <a href="samples/fsharp/getting-started/BinaryClassification_CreditCardFraudDetection">F#</a></b></td> </tr> <tr> <td align="middle"><img src="images/disease-detection.png" alt="disease detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>心脏病预测<br><a href="samples/csharp/getting-started/BinaryClassification_HeartDiseaseDetection">C#</a></td> <td></td> <td></td> </tr> <tr> <td align="middle" colspan="3">多类分类</td> </tr> <tr> <td align="middle"><img src="images/issue-labeler.png" alt="Issue Labeler chart"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>GitHub Issues 分类<br> <a href="samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler">C#</a> <a href="samples/fsharp/end-to-end-apps/MulticlassClassification-GitHubLabeler">F#</a></b></td> <td align="middle"><img src="images/flower-classification.png" alt="Movie Recommender chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>鸢尾花分类<br><a href="samples/csharp/getting-started/MulticlassClassification_Iris">C#</a> <a href="samples/fsharp/getting-started/MulticlassClassification_Iris">F#</a></b></td> <td align="middle"><img src="images/handwriting-classification.png" alt="Movie Recommender chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>手写数字识别<br><a href="samples/csharp/getting-started/MulticlassClassification_MNIST">C#</a></b></td> </tr> <tr> <td align="middle" colspan="3">建议</td> </tr> <tr> <td align="middle"><img src="images/product-recommendation.png" alt="Product Recommender chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>产品推荐<br><a href="samples/csharp/getting-started/MatrixFactorization_ProductRecommendation">C#</a></h4></td> <td align="middle"><img src="images/movie-recommendation.png" alt="Movie Recommender chart" ><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>电影推荐<br>(Matrix Factorization)<b><br><a href="samples/csharp/getting-started/MatrixFactorization_MovieRecommendation">C#</a></b></td> <td align="middle"><img src="images/movie-recommendation.png" alt="Movie Recommender chart"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>电影推荐<br>(Field Aware Factorization Machines)<br><a href="samples/csharp/end-to-end-apps/Recommendation-MovieRecommender">C#</a></b></td> </tr> <tr> <td align="middle" colspan="3">回归</td> </tr> <tr> <td align="middle"><img src="images/price-prediction.png" alt="Price Prediction chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>价格预测<br><a href="samples/csharp/getting-started/Regression_TaxiFarePrediction">C#</a> <a href="samples/fsharp/getting-started/Regression_TaxiFarePrediction">F#</a></b></td> <td align="middle"><br><img src="images/sales-forcasting.png" alt="Sales ForeCasting chart"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>销售预测<br><a href="samples/csharp/end-to-end-apps/Forecasting-Sales">C#</a><br><br></b></td> <td align="middle"><img src="images/demand-prediction.png" alt="Demand Prediction chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>需求预测<br><a href="samples/csharp/getting-started/Regression_BikeSharingDemand">C#</a> <a href="samples/fsharp/getting-started/Regression_BikeSharingDemand">F#</a></b></td> </tr> <tr> <td align="middle" colspan="3">时间序列预测</td> </tr> <tr> <td align="middle"><br><img src="images/sales-forcasting.png" alt="Sales ForeCasting chart"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>销售预测<br><a href="samples/csharp/end-to-end-apps/Forecasting-Sales">C#</a><br><br></b></td> <td></td> <td></td> </tr> <tr> <td align="middle" colspan="3">异常情况检测</td> </tr> <tr> <td align="middle"><img src="images/spike-detection.png" alt="Spike detection chart"><br><br><b>销售高峰检测<br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"> <a href="samples/csharp/getting-started/AnomalyDetection_Sales">C#</a>  <img src="images/app-type-e2e-black.png" alt="End-to-end app icon"> <a href="samples/csharp/end-to-end-apps/AnomalyDetection-Sales">C#</a><b></td> <td align="middle"><img src="images/spike-detection.png" alt="Spike detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>电力异常检测<br><a href="samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings">C#</a><b></td> <td align="middle"><img src="images/anomaly-detection.png" alt="Power Anomaly detection chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>信用卡欺诈检测<br>(Anomaly Detection)<br><a href="samples/csharp/getting-started/AnomalyDetection_CreditCardFraudDetection">C#</a><b></td> </tr> <tr> <td align="middle" colspan="3">聚类分析</td> </tr> <tr> <td align="middle"><img src="images/customer-segmentation.png" alt="Customer Segmentation chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>客户细分<br><a href="samples/csharp/getting-started/Clustering_CustomerSegmentation">C#</a> <a href="samples/fsharp/getting-started/Clustering_CustomerSegmentation">F#</a></b></td> <td align="middle"><img src="images/clustering.png" alt="IRIS Flowers chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>鸢尾花聚类<br><a href="samples/csharp/getting-started/Clustering_Iris">C#</a> <a href="samples/fsharp/getting-started/Clustering_Iris">F#</a></b></td> <td></td> </tr> <tr> <td align="middle" colspan="3">排名</td> </tr> <tr> <td align="middle"><img src="images/ranking-numbered.png" alt="Ranking chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>排名搜索引擎结果<br><a href="samples/csharp/getting-started/Ranking_Web">C#</a><b></td> <td></td> <td></td> </tr> <tr> <td align="middle" colspan="3">计算机视觉</td> </tr> <tr> <td align="middle"><img src="images/image-classification.png" alt="Image Classification chart"><br><b>图像分类训练<br> (High-Level API)<br> <img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"> <a href="samples/csharp/getting-started/DeepLearning_ImageClassification_Training">C#</a> <a href="samples/fsharp/getting-started/DeepLearning_ImageClassification_Training">F#</a>     </td> <td align="middle"><img src="images/image-classification.png" alt="Image Classification chart"><br><b>图像分类预测<br>(Pretrained TensorFlow model scoring)<br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"> <a href="samples/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow">C#</a> <a href="samples/fsharp/getting-started/DeepLearning_ImageClassification_TensorFlow">F#</a>     <img src="images/app-type-e2e-black.png" alt="End-to-end app icon"> <a href="samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TF">C#</a><b></td><b></td> <td align="middle"><img src="images/image-classification.png" alt="Image Classification chart"><br><b>图像分类训练<br> (TensorFlow Featurizer Estimator)<br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"> <a href="samples/csharp/getting-started/DeepLearning_TensorFlowEstimator">C#</a> <a href="samples/fsharp/getting-started/DeepLearning_TensorFlowEstimator">F#</a><b></td> </tr> <tr> <td align="middle"><br><img src="images/object-detection.png" alt="Object Detection chart"><br><b>对象检测<br> (ONNX model scoring)<br> <img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"> <a href="samples/csharp/getting-started/DeepLearning_ObjectDetection_Onnx">C#</a> <img src="images/app-type-e2e-black.png" alt="End-to-end app icon"> <a href="/samples/csharp/end-to-end-apps/ObjectDetection-Onnx">C#</a><b></td> </tr> </table> <br> <br> <br> <table > <tr> <td align="middle" colspan="3">跨领域方案</td> </tr> <tr> <td align="middle"><img src="images/web.png" alt="web image" ><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>Web API上的可扩展模型<br><a href="samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg">C#</a><b></td> <td align="middle"><img src="images/web.png" alt="web image" ><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>Razor Web应用程序上的可扩展模型<br><a href="samples/modelbuilder/BinaryClassification_Sentiment_Razor">C#</a><b></td> <td align="middle"><img src="images/azure-functions-20.png" alt="Azure functions logo"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>Azure Functions上的可扩展模型<br><a href="samples/csharp/end-to-end-apps/ScalableMLModelOnAzureFunction">C#</a><b></td> </tr> <tr> <td align="middle"><img src="images/smile.png" alt="Database chart"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>Blazor Web应用程序上的可扩展模型<br><a href="samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp">C#</a><b></td> <td align="middle"><img src="images/large-data-set.png" alt="large file chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>大数据集<br><a href="samples/csharp/getting-started/LargeDatasets">C#</a><b></td> <td align="middle"><img src="images/database.png" alt="Database chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>使用DatabaseLoader加载数据<br><a href="samples/csharp/getting-started/DatabaseLoader">C#</a><b></td> </tr> <tr> <td align="middle"><img src="images/database.png" alt="Database chart"><br><img src="images/app-type-getting-started-term-cursor.png" alt="Getting started icon"><br><b>使用LoadFromEnumerable加载数据<br><a href="samples/csharp/getting-started/DatabaseIntegration">C#</a><b></td> <td align="middle"><img src="images/model-explain-smaller.png" alt="Model explainability chart"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>模型可解释性<br><a href="samples/csharp/end-to-end-apps/Model-Explainability">C#</a></b></td> <td align="middle"><img src="images/extensibility.png" alt="Extensibility icon"><br><img src="images/app-type-e2e-black.png" alt="End-to-end app icon"><br><b>导出到ONNX<br><a href="samples/csharp/getting-started/Regression_ONNXExport">C#</a></b></td> </tr> </table>自动生成ML.NET模型（预览状态）
前面的示例向您展示了如何使用ML.NET API 1.0（发布于2019年5月）。
但是，我们还在努力通过其他技术简化ML.NET的使用，这样您就不需要自己编写代码来训练模型，只需提供数据集即可，ML.NET将为您自动为您自动生成“最佳”模型和运行它的代码。
这些用于自动生成模型的附加技术处于预览状态，目前只支持二进制分类、多类分类和回归。在未来的版本中，我们将支持额外的ML任务，如建议、异常检测、聚类分析等。
CLI示例:(预览状态）
ML.NET CLI（命令行界面）是一个可以在任何命令提示符（Windows，Mac或Linux）上运行的工具，用于根据您提供的训练数据集生成高质量的ML.NET模型。 此外，它还生成示例C＃代码以运行/评分该模型以及用于创建/训练它的C#代码，以便您可以研究它使用的算法和设置。
| CLI（命令行界面）示例 |
|---|
| 二元分类示例 |
| 多类分类示例 |
| 回归测试示例 |
自动化机器学习 API示例:(预览状态）
ML.NET AutoML API基本上是一组打包为NuGet包的库，您可以在.NET代码中使用它们。 AutoML消除了选择不同算法，超参数的任务。 AutoML将智能地生成许多算法和超参数组合，并为您找到高质量的模型。
| 自动化机器学习 API示例 |
|---|
| 二元分类示例 |
| 多类分类示例 |
| 排名示例 |
| 回归测试示例 |
| 高级实验示例 |
其他ML.NET社区示例
除了微软提供的ML.NET示例之外，我们还列出了社区创建的示例，这些示例位于单独的页面中： ML.NET 社区示例
这些社区示例不是由微软维护，而是由其所有者维护。 如果您已经创建了任何很酷的ML.NET示例，请将其信息添加到此REQUEST issue ，我们最终将在上面提到的页面发布其信息。
了解更多
教程，机器学习基础知识等详细信息，请参阅ML.NET指南 。
API参考
请查看ML.NET API参考，了解各种可用的 API。
贡献
我们欢迎贡献！ 请查看我们的贡献指南。
社区
请加入我们的Gitter社区 
这个项目采用了贡献者契约规定的行为准则，以表明我们社区的预期行为。有关更多信息，请参见.NET基金会行为准则。