public void BestFirstTreeBuilder_Build_Leaf_Nodes_4()
{
var(observations, targets) = DataSetUtilities.LoadGlassDataSet();
var sut = new DecisionTreeLearner(new BestFirstTreeBuilder(2000, 4, observations.ColumnCount, 0.000001, 42,
new OnlyUniqueThresholdsSplitSearcher(1), new GiniClassificationImpurityCalculator()));
var model = new ClassificationDecisionTreeModel(sut.Learn(observations, targets));
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.37383177570093457, actual, 0.00001);
}
public void BestFirstTreeBuilder_Build_Leaf_Nodes_4()
{
var parser = new CsvParser(() => new StringReader(Resources.Glass));
var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix();
var targets = parser.EnumerateRows("Target").ToF64Vector();
var rows = targets.Length;
var sut = new DecisionTreeLearner(new BestFirstTreeBuilder(2000, 4, observations.ColumnCount, 0.000001, 42,
new OnlyUniqueThresholdsSplitSearcher(1), new GiniClassificationImpurityCalculator()));
var model = new ClassificationDecisionTreeModel(sut.Learn(observations, targets));
var predictions = model.Predict(observations);
var evaluator = new TotalErrorClassificationMetric <double>();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.37383177570093457, actual, 0.00001);
}