numl is a very famous open source machine learning toolkit. As with most machine learning frameworks, it too uses the Iris dataset for many of its examples, including the one we will use for decision trees.

Here is an example of our numl output:

Let's take a look at the code behind that example:

static void Main(string[] args)
{
Console.WriteLine("Hello World!");
var description = Descriptor.Create<Iris>();
Console.WriteLine(description);
var generator = new DecisionTreeGenerator();
var data = Iris.Load();
var model = generator.Generate(description, data);
Console.WriteLine("Generated model:");
Console.WriteLine(model);
Console.ReadKey();
}

Definitely not the most complex of functions, is it? This is the beauty of using numl in your applications; it's incredibly easy to use and to integrate.

The preceding code creates a descriptor and DecisionTreeGenerator, loads the Iris dataset, and then generates a model. Here is just a sample of the data being loaded:

public static Iris[] Load()
{
return new Iris[]
{
new Iris { SepalLength = 5.1m, SepalWidth = 3.5m, PetalLength =
  1.4m, PetalWidth = 0.2m, Class = "Iris-setosa" },
new Iris { SepalLength = 4.9m, SepalWidth = 3m, PetalLength = 
  1.4m, PetalWidth = 0.2m, Class = "Iris-setosa" },
new Iris { SepalLength = 4.7m, SepalWidth = 3.2m, PetalLength =
  1.3m, PetalWidth = 0.2m, Class = "Iris-setosa" },
new Iris { SepalLength = 4.6m, SepalWidth = 3.1m, PetalLength = 
  1.5m, PetalWidth = 0.2m, Class = "Iris-setosa" },
new Iris { SepalLength = 5m, SepalWidth = 3.6m, PetalLength = 
  1.4m, PetalWidth = 0.2m, Class = "Iris-setosa" },
new Iris { SepalLength = 5.4m, SepalWidth = 3.9m, PetalLength = 
  1.7m, PetalWidth = 0.4m, Class = "Iris-setosa" },

And so on...