public void RegressionStackingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var(observations, targets) = DataSetUtilities.LoadDecisionTreeDataSet();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.LearnStackingModel(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.06951934687172627, actual, 0.0001);
}
public void RegressionStackingEnsembleLearner_CreateMetaFeatures_Then_Learn()
{
var learners = new IIndexedLearner <double>[]
{
new RegressionDecisionTreeLearner(2),
new RegressionDecisionTreeLearner(5),
new RegressionDecisionTreeLearner(7),
new RegressionDecisionTreeLearner(9)
};
var sut = new RegressionStackingEnsembleLearner(learners, new RegressionDecisionTreeLearner(9),
new RandomCrossValidation <double>(5, 23), false);
var parser = new CsvParser(() => new StringReader(Resources.DecisionTreeData));
var observations = parser.EnumerateRows("F1", "F2").ToF64Matrix();
var targets = parser.EnumerateRows("T").ToF64Vector();
var metaObservations = sut.LearnMetaFeatures(observations, targets);
var model = sut.LearnStackingModel(observations, metaObservations, targets);
var predictions = model.Predict(observations);
var evaluator = new MeanSquaredErrorRegressionMetric();
var actual = evaluator.Error(targets, predictions);
Assert.AreEqual(0.06951934687172627, actual, 0.0001);
}