public void ClassificationBackwardEliminationModelSelectingEnsembleLearner_CreateMetaFeatures_Then_Learn() { var learners = new IIndexedLearner <ProbabilityPrediction>[] { new ClassificationDecisionTreeLearner(2), new ClassificationDecisionTreeLearner(5), new ClassificationDecisionTreeLearner(7), new ClassificationDecisionTreeLearner(9), new ClassificationDecisionTreeLearner(11), new ClassificationDecisionTreeLearner(21), new ClassificationDecisionTreeLearner(23), new ClassificationDecisionTreeLearner(1), new ClassificationDecisionTreeLearner(14), new ClassificationDecisionTreeLearner(17), new ClassificationDecisionTreeLearner(19), new ClassificationDecisionTreeLearner(33) }; var sut = new ClassificationBackwardEliminationModelSelectingEnsembleLearner(learners, 5); var parser = new CsvParser(() => new StringReader(Resources.Glass)); var observations = parser.EnumerateRows(v => v != "Target").ToF64Matrix(); var targets = parser.EnumerateRows("Target").ToF64Vector(); var metaObservations = sut.LearnMetaFeatures(observations, targets); var model = sut.SelectModels(observations, metaObservations, targets); var predictions = model.PredictProbability(observations); var metric = new LogLossClassificationProbabilityMetric(); var actual = metric.Error(targets, predictions); Assert.AreEqual(0.52351727716455632, actual, 0.0001); }
public void ClassificationBackwardEliminationModelSelectingEnsembleLearner_CreateMetaFeatures_Then_Learn() { var learners = new IIndexedLearner <ProbabilityPrediction>[] { new ClassificationDecisionTreeLearner(2), new ClassificationDecisionTreeLearner(5), new ClassificationDecisionTreeLearner(7), new ClassificationDecisionTreeLearner(9), new ClassificationDecisionTreeLearner(11), new ClassificationDecisionTreeLearner(21), new ClassificationDecisionTreeLearner(23), new ClassificationDecisionTreeLearner(1), new ClassificationDecisionTreeLearner(14), new ClassificationDecisionTreeLearner(17), new ClassificationDecisionTreeLearner(19), new ClassificationDecisionTreeLearner(33) }; var sut = new ClassificationBackwardEliminationModelSelectingEnsembleLearner(learners, 5); var(observations, targets) = DataSetUtilities.LoadGlassDataSet(); var metaObservations = sut.LearnMetaFeatures(observations, targets); var model = sut.SelectModels(observations, metaObservations, targets); var predictions = model.PredictProbability(observations); var metric = new LogLossClassificationProbabilityMetric(); var actual = metric.Error(targets, predictions); Assert.AreEqual(0.52351727716455632, actual, 0.0001); }