Package quickml.supervised.classifier.decisionTree.scorers

Examples of quickml.supervised.classifier.decisionTree.scorers.MSEScorer

31
32
33
34
35
36
37
38
        parametersToOptimize.put(IGNORE_ATTR_PROB, new FixedOrderRecommender(0.5, 0.0, 0.1, 0.2, 0.4, 0.7, 0.8, 0.9, 0.95, 0.98, 0.99));
        parametersToOptimize.put(MAX_DEPTH, new FixedOrderRecommender(Integer.MAX_VALUE, 2, 3, 4, 5, 6, 7, 9));
        parametersToOptimize.put(MIN_SCORE, new FixedOrderRecommender(0.00000000000001, Double.MIN_VALUE, 0.0, 0.000001, 0.0001, 0.001, 0.01, 0.1));
        parametersToOptimize.put(MIN_CAT_ATTR_OCC, new FixedOrderRecommender(5, 0, 1, 64, 1024, 4098));
        parametersToOptimize.put(MIN_LEAF_INSTANCES, new FixedOrderRecommender(0, 10, 100, 1000, 10000, 100000));
        parametersToOptimize.put(SCORER, new FixedOrderRecommender(new MSEScorer(MSEScorer.CrossValidationCorrection.FALSE), new MSEScorer(MSEScorer.CrossValidationCorrection.TRUE), new SplitDiffScorer(), new InformationGainScorer(), new GiniImpurityScorer()));
        return parametersToOptimize;
    }
View Full Code Here
39
40
41
42
43
44
45
    private Set<String> splitModelWhiteList;
    private Serializable id;
    private Random rand = Random.Util.fromSystemRandom(MapUtils.random);

    public TreeBuilder() {
        this(new MSEScorer(MSEScorer.CrossValidationCorrection.FALSE));
    }
View Full Code Here
36
37
38
39
40
41
42
43
44
45
46
    }

    private static void testWithInstances(String dsName, final List<Instance<AttributesMap>> instances) {
        StationaryCrossValidator crossValidator = new StationaryCrossValidator(new ClassifierLogCVLossFunction());

        for (final Scorer scorer : Lists.newArrayList(new SplitDiffScorer(), new MSEScorer(MSEScorer.CrossValidationCorrection.FALSE), new MSEScorer(MSEScorer.CrossValidationCorrection.TRUE))) {
            final TreeBuilder singleTreeBuilder = new TreeBuilder(scorer).binaryClassification(true);
            System.out.println(dsName+", single-tree, "+scorer+", "+crossValidator.getCrossValidatedLoss(singleTreeBuilder, instances));

            TreeBuilder forestTreeBuilder = new TreeBuilder(scorer).ignoreAttributeAtNodeProbability(0.5).binaryClassification(true);
            RandomForestBuilder randomForestBuilder = new RandomForestBuilder(forestTreeBuilder).numTrees(100).executorThreadCount(8);
View Full Code Here
TOP

Related Classes of quickml.supervised.classifier.decisionTree.scorers.MSEScorer

  • quickml.Benchmarks
  • quickml.supervised.classifier.decisionTree.tree.ClassificationCounter
  • quickml.supervised.classifier.decisionTree.TreeBuilder
  • quickml.supervised.classifier.decisionTree.TreeBuilderFactory
    • Copyright © 2018 www.massapicom. All rights reserved.
    All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.