最近在搞机器学习，目前国内没有什么关于ML.NET的教程，官方都是一大堆英文，经过了我的努力，找到了大哥的博客，有关于ML.NET的内容

原文地址：https://www.cnblogs.com/BeanHsiang/p/9010267.html  

使用ML.NET直接从nuget中搜索ML.NET 安装到项目即可

去下载一个现成的数据集，复制粘贴其中的数据到任何一个文本编辑器中，然后保存命名为iris-data.txt到myApp目录中。

打开program.cs 以下代码：

using Microsoft.ML;using Microsoft.ML.Runtime.Api;using Microsoft.ML.Trainers;using Microsoft.ML.Transforms;using System;namespace myApp{    class Program    {        // STEP 1: Define your data structures        // IrisData is used to provide training data, and as         // input for prediction operations        // - First 4 properties are inputs/features used to predict the label        // - Label is what you are predicting, and is only set when training        public class IrisData        {            [Column("0")]            public float SepalLength;            [Column("1")]            public float SepalWidth;            [Column("2")]            public float PetalLength;            [Column("3")]            public float PetalWidth;            [Column("4")]            [ColumnName("Label")]            public string Label;        }        // IrisPrediction is the result returned from prediction operations        public class IrisPrediction        {            [ColumnName("PredictedLabel")]            public string PredictedLabels;        }        static void Main(string[] args)        {            // STEP 2: Create a pipeline and load your data            var pipeline = new LearningPipeline();            // If working in Visual Studio, make sure the 'Copy to Output Directory'             // property of iris-data.txt is set to 'Copy always'            string dataPath = "iris-data.txt";            pipeline.Add(new TextLoader
     
      (dataPath, separator: ","));            // STEP 3: Transform your data            // Assign numeric values to text in the "Label" column, because only            // numbers can be processed during model training            pipeline.Add(new Dictionarizer("Label"));            // Puts all features into a vector            pipeline.Add(new ColumnConcatenator("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"));            // STEP 4: Add learner            // Add a learning algorithm to the pipeline.             // This is a classification scenario (What type of iris is this?)            pipeline.Add(new StochasticDualCoordinateAscentClassifier());            // Convert the Label back into original text (after converting to number in step 3)            pipeline.Add(new PredictedLabelColumnOriginalValueConverter() { PredictedLabelColumn = "PredictedLabel" });            // STEP 5: Train your model based on the data set            var model = pipeline.Train
      
       ();            // STEP 6: Use your model to make a prediction            // You can change these numbers to test different predictions            var prediction = model.Predict(new IrisData()            {                SepalLength = 3.3f,                SepalWidth = 1.6f,                PetalLength = 0.2f,                PetalWidth = 5.1f,            });            Console.WriteLine($"Predicted flower type is: {prediction.PredictedLabels}");        }    }}