In the next few sections, we are going to look at some code samples without all the verbosity. This will be pure C# code so it should be something easily understood by all.

Let’s take a quick look at how we can use SharpLearning to predict observations. I’ll show you an entire code sample without the verbosity:

var parser = new CsvParser(() =>new StringReader(Resources.AptitudeData));
var observations = parser.EnumerateRows(v => v != "Pass").ToF64Matrix();
var targets = parser.EnumerateRows("Pass").ToF64Vector();
var rows = targets.Length;
var learner = new ClassificationDecisionTreeLearner(100, 1, 2, 0.001, 42);
varsut = learner.Learn(observations, targets);
var predictions = sut.Predict(observations);
var evaluator = new TotalErrorClassificationMetric<double>();
var error = evaluator.Error(targets, predictions);
Assert.AreEqual(0.038461538461538464, error, 0.0000001);