我正在使用 ML.NET 二進制分類算法和威斯康星州乳腺癌數(shù)據(jù)。訓(xùn)練模型后，我發(fā)現(xiàn)每個實例都被評估為錯誤。在我的測試文件中，我有 100 個實例。75 個負(fù)數(shù)和 25 個正數(shù)。因此，從指標(biāo)來看，準(zhǔn)確度為 0.75，負(fù)精度為 0.75。這意味著所有實例都使用 0（假）進行評估。private static string trainingDataPath = Path.Combine(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "uploads"), "data.csv");        private static string testDataPath = Path.Combine(Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "uploads"), "test.csv");        public bool checkDiagnostic (BreastCancerData input)        {             // set up a new machine learning context            var mlContext = new MLContext();            // load training and test data            var trainingDataView = mlContext.Data.LoadFromTextFile<BreastCancerData>(trainingDataPath, hasHeader: false, separatorChar: ',');            var testDataView = mlContext.Data.LoadFromTextFile<BreastCancerData>(testDataPath, hasHeader: false, separatorChar: ',');            // Preview the data.             //var dataPreview = trainingDataView.Preview(maxRows:700);            //var dataPreview2 = testDataView.Preview();            // the rest of the training code goes here...            var trainer = mlContext.BinaryClassification.Trainers.LinearSvm("Label", "Features");            var trainingPipeline = mlContext.Transforms.Concatenate(outputColumnName: "Features", nameof(BreastCancerData.AreaMean),                                nameof(BreastCancerData.AreaSe), nameof(BreastCancerData.AreaWorst), nameof(BreastCancerData.CompactnessMean),                                nameof(BreastCancerData.CompactnessSe), nameof(BreastCancerData.CompactnessWorst), nameof(BreastCancerData.ConcavePointsMean),                                nameof(BreastCancerData.ConcavePointsSe), nameof(BreastCancerData.ConcavePointsWorst), nameof(BreastCancerData.ConcavityMean),                                nameof(BreastCancerData.ConcavitySe), nameof(BreastCancerData.ConcavityWorst), nameof(BreastCancerData.FractalDimensionMean),