ML.NET |专为 .NET 设计的机器学习

为 .NET 开发者生成

借助 ML.NET，可以使用现有的 .NET 技能轻松地将 ML 集成到 .NET 应用中，而无需任何以前的 ML 体验。

使用 AutoML 简化自定义ML

ML.NET 提供 AutoML 和高效工具，可帮助用户轻松构建、训练和部署高质量自定义 ML 模型。

使用 TensorFlow & 进行扩展更多

ML.NET 允许你利用其他热门 ML 库(如 Infer.NET、TensorFlow 和 ONNX)来应对其他 ML 方案。

获得在规模的信任广泛验证

使用 Power BI、Microsoft Defender、Outlook 和 Bing 等公认的 Microsoft 产品使用的相同 ML 框架。

C#
F#

//Step 1. Create an ML Context
var ctx = new MLContext();

//Step 2. Read in the input data from a text file for model training
IDataView trainingData = ctx.Data
    .LoadFromTextFile<ModelInput>(dataPath, hasHeader: true);

//Step 3. Build your data processing and training pipeline
var pipeline = ctx.Transforms.Text
    .FeaturizeText("Features", nameof(SentimentIssue.Text))
    .Append(ctx.BinaryClassification.Trainers
        .LbfgsLogisticRegression("Label", "Features"));

//Step 4. Train your model
ITransformer trainedModel = pipeline.Fit(trainingData);

//Step 5. Make predictions using your trained model
var predictionEngine = ctx.Model
    .CreatePredictionEngine<ModelInput, ModelOutput>(trainedModel);

var sampleStatement = new ModelInput() { Text = "This is a horrible movie" };

var prediction = predictionEngine.Predict(sampleStatement);

//Step 1. Create an ML Context
let ctx = MLContext()

//Step 2. Read in the input data from a text file
let trainingData = ctx.Data.LoadFromTextFile<ModelInput>(dataPath, hasHeader=true)

//Step 3. Build your data processing and training pipeline
let scope = ctx.BinaryClassification.Trainers.LbfgsLogisticRegression("Label", "Features")
let pipeline = ctx.Transforms.Text.FeaturizeText("Features", "Text").Append(scope)

//Step 4. Train your model
let trainedModel = pipeline.Fit(trainingData)

//Step 5. Make predictions using your model
let predictionEngine = ctx.Model.CreatePredictionEngine<ModelInput, ModelOutput>(trainedModel)

let sampleStatement = { Label = false; Text = "This is a horrible movie" }

let prediction = predictionEngine.Predict(sampleStatement)