ModelBuilderClassification.cs

public class ModelBuilder
{
    private readonly string _trainingDataLocation;
    private readonly ILearningPipelineItem _algorythm;

    public ModelBuilder(string trainingDataLocation, ILearningPipelineItem algorythm)
    {
        _trainingDataLocation = trainingDataLocation;
        _algorythm = algorythm;
    }

    /// <summary>
    /// Using training data location that is passed trough constructor this method is building
    /// and training machine learning model.
    /// </summary>
    /// <returns>Trained machine learning model.</returns>
    public PredictionModel<WineQualitySample, WineQualityPrediction> BuildAndTrain()
    {
        var pipeline = new LearningPipeline();
        pipeline.Add(new TextLoader(_trainingDataLocation).CreateFrom<WineQualitySample>(useHeader: true, separator: ';'));
        pipeline.Add(new MissingValueSubstitutor("FixedAcidity") { ReplacementKind = NAReplaceTransformReplacementKind.Mean});
        pipeline.Add(MakeNormalizer());
        pipeline.Add(new ColumnConcatenator("Features",
                                             "FixedAcidity",
                                             "VolatileAcidity",
                                             "CitricAcid",
                                             "ResidualSugar",
                                             "Chlorides",
                                             "FreeSulfurDioxide",
                                             "TotalSulfurDioxide",
                                             "Density",
                                             "Ph",
                                             "Sulphates",
                                             "Alcohol"));
        pipeline.Add(_algorythm);

        return pipeline.Train<WineQualitySample, WineQualityPrediction>();
    }

    private ILearningPipelineItem MakeNormalizer()
    {
        var normalizer = new BinNormalizer();
        normalizer.NumBins = 2;
        normalizer.AddColumn("Label");

        return normalizer;
    }
}