Load a List of Objects as Dataset in ML.NET

In ML.NET, you can load a list of objects as a dataset using the DataView API. ML.NET provides a flexible way to represent data as DataView, which can be consumed by machine learning algorithms. To do this, you’ll need to follow these steps:

Define the class for your data objects: Create a class that represents the structure of your data. Each property of the class corresponds to a feature in your dataset.
Create a list of data objects: Instantiate a list of objects with your data. Each object in the list represents one data point.
Convert the list to a DataView: Use the MLContext class to create a DataView from the list of objects.

Here’s a step-by-step implementation:

Step 1: Define the class for your data objects

Assuming you have a class DataObject with properties Feature1, Feature2, and Label, it should look like this:

public class DataObject
{
    public float Feature1 { get; set; }
    public float Feature2 { get; set; }
    public float Label { get; set; }
}

Step 2: Create a list of data objects

Create a list of DataObject instances containing your data points:

var dataList = new List<DataObject>
{
    new DataObject { Feature1 = 1.2f, Feature2 = 5.4f, Label = 0.8f },
    new DataObject { Feature1 = 2.1f, Feature2 = 3.7f, Label = 0.5f },
    // Add more data points here
};

Step 3: Convert the list to a DataView

Use the MLContext class to create a DataView from the list of objects:

using System;
using System.Collections.Generic;
using Microsoft.ML;

// ...

var mlContext = new MLContext();

// Convert the list to a DataView
var dataView = mlContext.Data.LoadFromEnumerable(dataList);

Now you have the dataView, which you can use to train and evaluate your machine learning model in ML.NET. The DataView can be directly consumed by ML.NET’s algorithms or be pre-processed using data transformations.

Remember to replace DataObject with your actual class and modify the properties accordingly based on your dataset.

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