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);
}
ClassificationDecisionTreeModel CreateTree(F64Matrix observations, double[] targets, int[] indices, Random random)
{
var learner = new DecisionTreeLearner(
new DepthFirstTreeBuilder(m_maximumTreeDepth,
m_featuresPrSplit,
m_minimumInformationGain,
m_random.Next(),
new RandomSplitSearcher(m_minimumSplitSize, m_random.Next()),
new GiniClasificationImpurityCalculator()));
var treeIndicesLength = (int)Math.Round(m_subSampleRatio * (double)indices.Length);
var treeIndices = new int[treeIndicesLength];
for (int j = 0; j < treeIndicesLength; j++)
{
treeIndices[j] = indices[random.Next(indices.Length)];
}
var model = new ClassificationDecisionTreeModel(learner.Learn(observations, targets, treeIndices));
return(model);
}