CODEWITHSUNDEEP
machine-learninglinear-regressionml.net
Sarthak Agrawal
Sarthak Agrawal

Reputation: 586

Understanding Score column in ml.net regression

I am following this tutorial from Microsoft docs for linear regression using ML.Net. I am not able to determine why exactly do we need a class with a score column with the specific column name as 'Score'. I tried changing the column name from Score to something else which results in the following exception:
enter image description here


The classes I have declared for my Input and predictions are as follows:
public class TaxiTrip {
    // All the columns inclusive of the label column
    [LoadColumn(0)]
    public string VendorId;

    [LoadColumn(1)]
    public string RateCode;

    [LoadColumn(2)]
    public float PassengerCount;

    [LoadColumn(3)]
    public float TripTime;

    [LoadColumn(4)]
    public float TripDistance;

    [LoadColumn(5)]
    public string PaymentType;

    [LoadColumn(6)]
    public float FareAmount;
}

public class TaxiTripFarePrediction {
    [ColumnName("PredictionScore")]
    public float FareAmount;
}

Also as I never specified the class TaxiTripFarePrediction anywhere either in the training function or in the Evaluate function as shown above how does the model work when I specify the column name as Score instead of something else?

The similar scenario was seen for the Label column.

Upvotes: 1

Views: 1430

Answers (1)

Truc
Truc

Reputation: 462

The TaxiTripFarePrediction class represents predicted results. It has a single float field, FareAmount, with a Score attribute applied. In case of the regression task, the Score column contains predicted label values.

I'm new in ML and I think Score and Features cannot change the properties. You can refer this link: https://learn.microsoft.com/en-us/dotnet/machine-learning/how-does-mldotnet-work#mlnet-architecture

You can see this image. Score and Features are generated by algorithms enter image description here

Upvotes: 1

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