private static ITransformer BuildAndTrainModel(MLContext mLContext, IDataView splitTrainSet)
{
var path = IModelBuilder.GetAbsolutePath(@"Model\Model.zip");
var estimator = mLContext.Transforms.Text.FeaturizeText(outputColumnName: "Features", inputColumnName: nameof(Model.ModelInput.SentimentText));
var trainer = mLContext.BinaryClassification.Trainers.SdcaLogisticRegression(labelColumnName: "Label", featureColumnName: "Features");
var trainingPipelne = estimator.Append(trainer);
Console.WriteLine("=============== Create and Train the Model ===============");
var model = trainingPipelne.Fit(splitTrainSet);
Console.WriteLine("=============== End of training ===============");
Console.WriteLine("=============== Save Model ===============");
mLContext.Model.Save(model, splitTrainSet.Schema, path);
Console.WriteLine("=============== End of saving ===============");
Console.WriteLine();
return(model);
}
