Example usage for weka.classifiers Evaluation pctIncorrect

Introduction

In this page you can find the example usage for weka.classifiers Evaluation pctIncorrect.

Prototype

public final double pctIncorrect() 

Source Link

Document

Gets the percentage of instances incorrectly classified (that is, for which an incorrect prediction was made).

Usage

From source file:org.openml.webapplication.fantail.dc.landmarking.J48BasedLandmarker.java

License:Open Source License

public Map<String, Double> characterize(Instances data) {

    int numFolds = m_NumFolds;

    double score1 = 0.5;
    double score2 = 0.5;
    // double score3 = 0.5;

    double score3 = 0.5;
    double score4 = 0.5;
    // double score3 = 0.5;

    double score5 = 0.5;
    double score6 = 0.5;

    double score7 = 0.5;
    double score8 = 0.5;
    double score9 = 0.5;

    weka.classifiers.trees.J48 cls = new weka.classifiers.trees.J48();
    cls.setConfidenceFactor(0.00001f);//from w  w  w.ja  v a2 s  .  co m

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);

        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score1 = eval.pctIncorrect();
        score2 = eval.weightedAreaUnderROC();

        score7 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.J48();
    cls.setConfidenceFactor(0.0001f);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score3 = eval.pctIncorrect();
        score4 = eval.weightedAreaUnderROC();

        score8 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.J48();
    cls.setConfidenceFactor(0.001f);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score5 = eval.pctIncorrect();
        score6 = eval.weightedAreaUnderROC();

        score9 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    Map<String, Double> qualities = new HashMap<String, Double>();
    qualities.put(ids[0], score1);
    qualities.put(ids[1], score2);
    qualities.put(ids[2], score3);
    qualities.put(ids[3], score4);
    qualities.put(ids[4], score5);
    qualities.put(ids[5], score6);
    qualities.put(ids[6], score7);
    qualities.put(ids[7], score8);
    qualities.put(ids[8], score9);
    return qualities;
}

---

From source file:org.openml.webapplication.fantail.dc.landmarking.RandomTreeBasedLandmarker2.java

License:Open Source License

public Map<String, Double> characterize(Instances data) {

    int seed = m_Seed;
    Random r = new Random(seed);

    int numFolds = m_NumFolds;

    double score1 = 0.5;
    double score2 = 0.5;
    // double score3 = 0.5;

    double score3 = 0.5;
    double score4 = 0.5;
    // double score3 = 0.5;

    double score5 = 0.5;
    double score6 = 0.5;

    weka.classifiers.trees.RandomTree cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());/*w  w  w  . j a v  a 2  s .  c  o  m*/
    cls.setKValue(m_K);
    // cls.setMaxDepth(1);

    try {
        // ds.buildClassifier(data);
        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score1 = eval.pctIncorrect();
        score2 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(2);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score3 = eval.pctIncorrect();
        score4 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(3);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score5 = eval.pctIncorrect();
        score6 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(4);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.RandomTree();
    cls.setSeed(r.nextInt());
    cls.setKValue(m_K);
    // cls.setMaxDepth(5);

    try {
        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

    } catch (Exception e) {
        e.printStackTrace();
    }

    Map<String, Double> qualities = new HashMap<String, Double>();
    qualities.put(ids[0], score1);
    qualities.put(ids[1], score2);
    qualities.put(ids[2], score3);
    qualities.put(ids[3], score4);
    qualities.put(ids[4], score5);
    qualities.put(ids[5], score6);
    return qualities;
}

---

From source file:org.openml.webapplication.fantail.dc.landmarking.REPTreeBasedLandmarker.java

License:Open Source License

public Map<String, Double> characterize(Instances data) {

    int numFolds = m_NumFolds;

    double score1 = 0.5;
    double score2 = 0.5;
    // double score3 = 0.5;

    double score3 = 0.5;
    double score4 = 0.5;
    // double score3 = 0.5;

    double score5 = 0.5;
    double score6 = 0.5;

    double score7 = 0.5;
    double score8 = 0.5;
    double score9 = 0.5;

    weka.classifiers.trees.REPTree cls = new weka.classifiers.trees.REPTree();
    cls.setMaxDepth(1);//from ww w .j a v a 2s . c o  m

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score1 = eval.pctIncorrect();
        score2 = eval.weightedAreaUnderROC();

        score7 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.REPTree();
    cls.setMaxDepth(2);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score3 = eval.pctIncorrect();
        score4 = eval.weightedAreaUnderROC();

        score8 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    //
    cls = new weka.classifiers.trees.REPTree();
    cls.setMaxDepth(3);

    try {

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(cls, data, numFolds, new java.util.Random(1));

        score5 = eval.pctIncorrect();
        score6 = eval.weightedAreaUnderROC();

        score9 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    Map<String, Double> qualities = new HashMap<String, Double>();
    qualities.put(ids[0], score1);
    qualities.put(ids[1], score2);
    qualities.put(ids[2], score3);
    qualities.put(ids[3], score4);
    qualities.put(ids[4], score5);
    qualities.put(ids[5], score6);
    qualities.put(ids[6], score7);
    qualities.put(ids[7], score8);
    qualities.put(ids[8], score9);
    return qualities;
}

---

From source file:org.openml.webapplication.fantail.dc.landmarking.SimpleLandmarkers.java

License:Open Source License

public Map<String, Double> characterize(Instances data) {

    int numFolds = m_NumFolds;

    double score1 = 0.5;
    double score2 = 0.5;

    double score5 = 0.5;
    double score6 = 0.5;

    double score3 = 0.5;
    double score4 = 0.5;

    weka.classifiers.trees.DecisionStump ds = new weka.classifiers.trees.DecisionStump();
    try {/*ww  w.j  a  v a2 s  .co m*/

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(ds, data, numFolds, new java.util.Random(1));

        score1 = eval.pctIncorrect();
        score2 = eval.weightedAreaUnderROC();
        score3 = eval.kappa();
    } catch (Exception e) {
        e.printStackTrace();
    }

    try {
        weka.classifiers.bayes.NaiveBayes nb = new weka.classifiers.bayes.NaiveBayes();

        weka.classifiers.Evaluation eval = new weka.classifiers.Evaluation(data);
        eval.crossValidateModel(nb, data, numFolds, new java.util.Random(1));

        score5 = eval.pctIncorrect();
        score6 = eval.weightedAreaUnderROC();
        score4 = eval.kappa();

    } catch (Exception e) {
        e.printStackTrace();
    }

    Map<String, Double> qualities = new HashMap<String, Double>();
    qualities.put(ids[0], score1);
    qualities.put(ids[1], score2);
    qualities.put(ids[2], score5);
    qualities.put(ids[3], score6);
    qualities.put(ids[4], score3);
    qualities.put(ids[5], score4);
    return qualities;
}

---

• «
• 1
• 2
• »